A survey on evaluation methods for image segmentation
---
paper_title: Performance Characterization in Computer Vision

paper_content:

Computer vision algorithms are composed of different sub-algorithms often applied in sequence. Determination of the performance of a total computer vision algorithm is possible if the performance of each of the sub-algorithm constituents is given. The problem, however, is that for most published algorithms, there is no performance characterization which has been established in the research literature. This is an awful state of affairs for the engineers whose job it is to design and build image analysis or machine vision systems.
---
paper_title: Computational Techniques in the Visual Segmentation of Static Scenes.

paper_content:

A wide range of segmentation techniques continues to evolve in the literature on scene analysis. Many of these approaches have been constrained to limited applications or goals. This survey analyzes the complexities encountered in applying these techniques to color images of natural scenes involving complex textured objects. It also explores new ways of using the techniques to overcome some of the problems which are described. An outline of considerations in the development of a general image segmentation system which can provide input to a semantic interpretation process is distributed throughout the paper. In particular, the problems of feature selection and extraction in images with textural variations are discussed. The approaches to segmentation are divided into two broad categories, boundary formation and region formation. The tools for extraction of boundaries involve spatial differentiation, nonmaxima suppression, relaxation processes, and grouping of local edges into segments. Approaches to region formation include region growing under local spatial guidance, histograms for analysis of global feature activity, and finally an integration of the strengths of each by a spatial analysis of feature activity. A brief discussion of attempts by others to integrate the segmentation and interpretation phrases is also provided. The discussion is supported by a variety of experimental results.
---
paper_title: A survey of threshold selection techniques

paper_content:

Abstract The use of thresholding as a tool in image segmentation has been extensively studied, and a variety of techniques have been proposed for automatic threshold selection. This paper presents a review of these techniques, including global, local, and dynamic methods.
---
paper_title: Segmentation evaluation using ultimate measurement accuracy

paper_content:

As a wide range of segmentation techniques have been developed in the last two decades, the evaluation and comparison of segmentation techniques becomes indispensable. In this paper, after a thorough review of previous work, we present a general approach for evaluation and comparison of segmentation techniques. More specifically, under this general framework, we propose to use the ultimate measurement accuracy to assess the performance of different algorithms. In image analysis, the ultimate goals of segmentation and other processing are often to obtain measurements of the object features in the image. Therefore, the accuracy of those ultimate measurements over segmented images would be a good index revealing the performance of segmentation techniques. We feel this measure is of much greater importance than, e.g., error probabilities on pixel labeling, or even specially developed figure of merit. There exist many features describing the properties of the objects in the image. Some of them are discussed here and their applicability and performance in the context of segmentation evaluation are studied. Based on experimental results, we provide some useful guidelines for choosing specific measurements for different evaluation situations and for selecting adequate techniques in particular segmentation applications.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
---
paper_title: A survey on image segmentation

paper_content:

Abstract For the past decade, many image segmentation techniques have been proposed. These segmentation techniques can be categorized into three classes, (1) characteristic feature thresholding or clustering, (2) edge detection, and (3) region extraction. This survey summarizes some of these techniques. In the area of biomedical image segmentation, most proposed techniques fall into the categories of characteristic feature thresholding or clustering and edge detection.
---
paper_title: Objective and quantitative segmentation evaluation and comparison

paper_content:

Abstract A general framework for segmentation evaluation is introduced after a brief review of previous work. The accuracy of object feature measurement is proposed as a criterion for judging the quality of segmentation results and assessing the performance of applied algorithms. This goal-oriented approach has been shown useful for an objective and quantitative study of segmentation techniques.
---
paper_title: A survey of thresholding techniques

paper_content:

Abstract In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.
---
paper_title: Transition region determination based thresholding

paper_content:

Abstract We present a newly developed thresholding technique which is not based on the image's gray-level histogram. This technique is fully automatic and quite robust in the presence of noise and unexpected structures. Moreover, no empirical parameters are used, and no limitations on shape and size of objects are imposed. A comparison with histogram based threshold selection is also discussed.
---
paper_title: Image Structure Representation and Processing: A Discussion of Some Segmentation Methods in Cytology

paper_content:

Image processing methods (segmentation) are presented in connection with a modeling of image structure. An image is represented as a set of primitives, characterized by their type, abstraction level, and a list of attributes. Entities (regions for example) are then described as a subset of primitives obeying particular rules. Image segmentation methods are discussed, according to the associated image modeling level. Their potential efficacity is compared, when applied to cytologic image analysis.
---
paper_title: Quantitative design and evaluation of enhancement/thresholding edge detectors

paper_content:

Quantitative design and performance evaluation techniques are developed for the enhancement/thresholding class of image edge detectors. The design techniques are based on statistical detection theory and deterministic pattern-recognition classification procedures. The performance evaluation methods developed include: a)deterministic measurement of the edge gradient amplitude; b)comparison of the probabilities of correct and false edge detection; and c) figure of merit computation. The design techniques developed are used to optimally design a variety of small and large mask edge detectors. Theoretical and experimental comparisons of edge detectors are presented.
---
paper_title: Image segmentation and image models

paper_content:

This paper discusses image segmentation techniques from the standpoint of the assumptions that an image should satisfy in order for a particular technique to be applicable to it. These assumptions, which are often not stated explicitly, can be regarded as (perhaps informal) "models" for classes of images. The paper emphasizes two basic classes of models: statistical models that describe the pixel population in an image or region, and spatial models that describe the decomposition of an image into regions.
---
paper_title: Segmentation of microscopic cell scenes.

paper_content:

Different methods for the automated segmentation of microscopic cell scenes are presented with examples. The techniques discussed include edge detection by thresholding, "blob" detection by split-and-merge algorithm, global thresholding using gray-level histograms, hierarchic thresholding using color information, global thresholding using two-dimensional histograms and segmentation by "blob" labeling. Methods are more robust against insignificant changes in the scene and perform more reliably as more a priori knowledge about the scene is incorporated in the segmentation algorithm. The inclusion of both photometric and geometric a priori knowledge can result in a high level of correct segmentations, the cost of which is increased computation time.
---
paper_title: Computer and Robot Vision

paper_content:

From the Publisher: ::: This two-volume set is an authoritative, comprehensive, modern work on computer vision that covers all of the different areas of vision with a balanced and unified approach. The discussion in "Volume I" focuses on image in, and image out or feature set out. "Volume II" covers the higher level techniques of illumination, perspective projection, analytical photogrammetry, motion, image matching, consistent labeling, model matching, and knowledge-based vision systems.
---
paper_title: Dynamic Measurement of Computer Generated Image Segmentations

paper_content:

This paper introduces a general purpose performance measurement scheme for image segmentation algorithms. Performance parameters that function in real-time distinguish this method from previous approaches that depended on an a priori knowledge of the correct segmentation. A low level, context independent definition of segmentation is used to obtain a set of optimization criteria for evaluating performance. Uniformity within each region and contrast between adjacent regions serve as parameters for region analysis. Contrast across lines and connectivity between them represent measures for line analysis. Texture is depicted by the introduction of focus of attention areas as groups of regions and lines. The performance parameters are then measured separately for each area. The usefulness of this approach lies in the ability to adjust the strategy of a system according to the varying characteristics of different areas. This feedback path provides the means for more efficient and error-free processing. Results from areas with dissimilar properties show a diversity in the measurements that is utilized for dynamic strategy setting.
---
paper_title: Image thresholding: Some new techniques

paper_content:

Abstract Some of the existing threshold selection techniques have been critically reviewed. Two algorithms based on a new conditional entropy measure of a partitioned image have been formulated. The approximate minimum error thresholding algorithm of Kittler and Illingworth has been implemented considering the Poisson distribution for the gray level instead of the commonly used normal distribution. Justification in support of the Poisson distribution has also been given. This method is found to be much better both from the point of view of convergence and segmented output. The proposed methods have been applied on a number of images and are found to produce good results. Objective evaluation of the thresholds has been done using divergence, region uniformity, correlation between original image and the segmented image, and second order entropy.
---
paper_title: Threshold Evaluation Techniques

paper_content:

Threshold selection techniques have been used as a basic tool in image segmentation, but little work has been done on the problem of evaluating a threshold of an image. The problem of threshold evaluation is addressed, and two methods are proposed for measuring the "goodness" of a thresholded image, one based on a busyness criterion and the other based on a discrepancy or error criterion. These evaluation techniques are applied to a set of infrared images and are shown to be useful in facilitating threshold selection. In fact, both methods usually result in similar or identical thresholds which yield good segmentations of the images.
---
paper_title: Textural Features for Image Classification

paper_content:

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
---
paper_title: Low Level Image Segmentation: An Expert System

paper_content:

A major problem in robotic vision is the segmentation of images of natural scenes in order to understand their content. This paper presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include metarules that embody inferences about the order in which the knowledge rules are matched. They also incorporate focus of attention rules that determine the path of processing within the image. Furthermore, an additional set of higher level rules dynamically alters the processing strategy. This paper discusses the structure and content of the knowledge and control rules for image segmentation.
---
paper_title: Dynamic Measurement of Computer Generated Image Segmentations

paper_content:

This paper introduces a general purpose performance measurement scheme for image segmentation algorithms. Performance parameters that function in real-time distinguish this method from previous approaches that depended on an a priori knowledge of the correct segmentation. A low level, context independent definition of segmentation is used to obtain a set of optimization criteria for evaluating performance. Uniformity within each region and contrast between adjacent regions serve as parameters for region analysis. Contrast across lines and connectivity between them represent measures for line analysis. Texture is depicted by the introduction of focus of attention areas as groups of regions and lines. The performance parameters are then measured separately for each area. The usefulness of this approach lies in the ability to adjust the strategy of a system according to the varying characteristics of different areas. This feedback path provides the means for more efficient and error-free processing. Results from areas with dissimilar properties show a diversity in the measurements that is utilized for dynamic strategy setting.
---
paper_title: A survey of thresholding techniques

paper_content:

Abstract In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.
---
paper_title: Dynamic Measurement of Computer Generated Image Segmentations

paper_content:

This paper introduces a general purpose performance measurement scheme for image segmentation algorithms. Performance parameters that function in real-time distinguish this method from previous approaches that depended on an a priori knowledge of the correct segmentation. A low level, context independent definition of segmentation is used to obtain a set of optimization criteria for evaluating performance. Uniformity within each region and contrast between adjacent regions serve as parameters for region analysis. Contrast across lines and connectivity between them represent measures for line analysis. Texture is depicted by the introduction of focus of attention areas as groups of regions and lines. The performance parameters are then measured separately for each area. The usefulness of this approach lies in the ability to adjust the strategy of a system according to the varying characteristics of different areas. This feedback path provides the means for more efficient and error-free processing. Results from areas with dissimilar properties show a diversity in the measurements that is utilized for dynamic strategy setting.
---
paper_title: A survey of thresholding techniques

paper_content:

Abstract In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.
---
paper_title: Validation of the interleaved pyramid for the segmentation of 3D vector images

paper_content:

Abstract A multiresolution pyramid with double scale space sampling (compared to the Burt & Hong scheme) for the segmentation of 3D images, of which the elements are multiple valued, is described. Evaluation is carried out by quality constrained cost analysis (QCCA).
---
paper_title: Performance Characterization in Computer Vision

paper_content:

Computer vision algorithms are composed of different sub-algorithms often applied in sequence. Determination of the performance of a total computer vision algorithm is possible if the performance of each of the sub-algorithm constituents is given. The problem, however, is that for most published algorithms, there is no performance characterization which has been established in the research literature. This is an awful state of affairs for the engineers whose job it is to design and build image analysis or machine vision systems.
---
paper_title: Performance Characterization in Computer Vision

paper_content:

Computer vision algorithms are composed of different sub-algorithms often applied in sequence. Determination of the performance of a total computer vision algorithm is possible if the performance of each of the sub-algorithm constituents is given. The problem, however, is that for most published algorithms, there is no performance characterization which has been established in the research literature. This is an awful state of affairs for the engineers whose job it is to design and build image analysis or machine vision systems.
---
paper_title: Threshold Evaluation Techniques

paper_content:

Threshold selection techniques have been used as a basic tool in image segmentation, but little work has been done on the problem of evaluating a threshold of an image. The problem of threshold evaluation is addressed, and two methods are proposed for measuring the "goodness" of a thresholded image, one based on a busyness criterion and the other based on a discrepancy or error criterion. These evaluation techniques are applied to a set of infrared images and are shown to be useful in facilitating threshold selection. In fact, both methods usually result in similar or identical thresholds which yield good segmentations of the images.
---
paper_title: Error measures for scene segmentation

paper_content:

Abstract Scene segmentation is an important problem in pattern recognition. Current subjective methods for evaluation and comparison of scene segmentation techniques are inadequate and objective quantitative measures are desirable. Two error measures, the percentage area misclassified ( p ) and a new pixel distance error (ϵ) were defined and evaluated in terms of their correlation with human observation for comparison of multiple segmentations of the same scene and multiple scenes segmented by the same technique. The results indicate that both these measures can be helpful in the evaluation and comparison of scene segmentation procedures.
---
paper_title: Segmentation evaluation using ultimate measurement accuracy

paper_content:

As a wide range of segmentation techniques have been developed in the last two decades, the evaluation and comparison of segmentation techniques becomes indispensable. In this paper, after a thorough review of previous work, we present a general approach for evaluation and comparison of segmentation techniques. More specifically, under this general framework, we propose to use the ultimate measurement accuracy to assess the performance of different algorithms. In image analysis, the ultimate goals of segmentation and other processing are often to obtain measurements of the object features in the image. Therefore, the accuracy of those ultimate measurements over segmented images would be a good index revealing the performance of segmentation techniques. We feel this measure is of much greater importance than, e.g., error probabilities on pixel labeling, or even specially developed figure of merit. There exist many features describing the properties of the objects in the image. Some of them are discussed here and their applicability and performance in the context of segmentation evaluation are studied. Based on experimental results, we provide some useful guidelines for choosing specific measurements for different evaluation situations and for selecting adequate techniques in particular segmentation applications.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
---
paper_title: Error measures for scene segmentation

paper_content:

Abstract Scene segmentation is an important problem in pattern recognition. Current subjective methods for evaluation and comparison of scene segmentation techniques are inadequate and objective quantitative measures are desirable. Two error measures, the percentage area misclassified ( p ) and a new pixel distance error (ϵ) were defined and evaluated in terms of their correlation with human observation for comparison of multiple segmentations of the same scene and multiple scenes segmented by the same technique. The results indicate that both these measures can be helpful in the evaluation and comparison of scene segmentation procedures.
---
paper_title: Computer and Robot Vision

paper_content:

From the Publisher: ::: This two-volume set is an authoritative, comprehensive, modern work on computer vision that covers all of the different areas of vision with a balanced and unified approach. The discussion in "Volume I" focuses on image in, and image out or feature set out. "Volume II" covers the higher level techniques of illumination, perspective projection, analytical photogrammetry, motion, image matching, consistent labeling, model matching, and knowledge-based vision systems.
---
paper_title: Evaluation of edge detection algorithms

paper_content:

In the past two decades several algorithms have been developed to extract the contour of homogeneous regions within digital images. A lot of the attention is focused to edge detection, being a crucial part in most of the algorithms. The classical edge operators emphasize the high frequency components in the image and therefore act poorly in cases of moderate low SNR and/or low spatial resolution of the imaging device. The awareness of this has lead to new approaches in which balanced trade-offs are sought between noise suppression, image deblurring and the ability to resolve interfering edges, altogether resulting in operators acting like bandpass filters. The ultimate goal of this work is to arrive at an evaluation scheme with criteria reflecting the requirements issuing the major application of edge detectors: contour extraction.
---
paper_title: Image segmentation and image models

paper_content:

This paper discusses image segmentation techniques from the standpoint of the assumptions that an image should satisfy in order for a particular technique to be applicable to it. These assumptions, which are often not stated explicitly, can be regarded as (perhaps informal) "models" for classes of images. The paper emphasizes two basic classes of models: statistical models that describe the pixel population in an image or region, and spatial models that describe the decomposition of an image into regions.
---
paper_title: Segmentation of microscopic cell scenes.

paper_content:

Different methods for the automated segmentation of microscopic cell scenes are presented with examples. The techniques discussed include edge detection by thresholding, "blob" detection by split-and-merge algorithm, global thresholding using gray-level histograms, hierarchic thresholding using color information, global thresholding using two-dimensional histograms and segmentation by "blob" labeling. Methods are more robust against insignificant changes in the scene and perform more reliably as more a priori knowledge about the scene is incorporated in the segmentation algorithm. The inclusion of both photometric and geometric a priori knowledge can result in a high level of correct segmentations, the cost of which is increased computation time.
---
paper_title: Three-dimensional image segmentation using a split, merge and group approach

paper_content:

A 3-D segmentation algorithm is presented, based on a split, merge and group approach. It uses a mixed (oct/quad)tree implementation. A number of homogeneity criteria is discussed and evaluated. An example shows the segmentation of mythramycin stained cell nuclei.
---
paper_title: Image Structure Representation and Processing: A Discussion of Some Segmentation Methods in Cytology

paper_content:

Image processing methods (segmentation) are presented in connection with a modeling of image structure. An image is represented as a set of primitives, characterized by their type, abstraction level, and a list of attributes. Entities (regions for example) are then described as a subset of primitives obeying particular rules. Image segmentation methods are discussed, according to the associated image modeling level. Their potential efficacity is compared, when applied to cytologic image analysis.
---
paper_title: Quantitative design and evaluation of enhancement/thresholding edge detectors

paper_content:

Quantitative design and performance evaluation techniques are developed for the enhancement/thresholding class of image edge detectors. The design techniques are based on statistical detection theory and deterministic pattern-recognition classification procedures. The performance evaluation methods developed include: a)deterministic measurement of the edge gradient amplitude; b)comparison of the probabilities of correct and false edge detection; and c) figure of merit computation. The design techniques developed are used to optimally design a variety of small and large mask edge detectors. Theoretical and experimental comparisons of edge detectors are presented.
---
paper_title: Three-dimensional image segmentation using a split, merge and group approach

paper_content:

A 3-D segmentation algorithm is presented, based on a split, merge and group approach. It uses a mixed (oct/quad)tree implementation. A number of homogeneity criteria is discussed and evaluated. An example shows the segmentation of mythramycin stained cell nuclei.
---
paper_title: Scene-segmentation algorithm development using error measures.

paper_content:

Development of scene-segmentation algorithms has generally been an ad hoc process. This paper presents a systematic technique for developing these algorithms using error-measure minimization. If scene segmentation is regarded as a problem of pixel classification whereby each pixel of a scene is assigned to a particular object class, development of a scene-segmentation algorithm becomes primarily a process of feature selection. In this study, four methods of feature selection were used to develop segmentation techniques for cervical cytology images: (1) random selection, (2) manual selection (best features in the subjective judgment of the investigator), (3) eigenvector selection (ranking features according to the largest contribution to each eigenvector of the feature covariance matrix) and (4) selection using the scene-segmentation error measure A2. Four features were selected by each method from a universe of 35 features consisting of gray level, color, texture and special pixel neighborhood features in 40 cervical cytology images . Evaluation of the results was done with a composite of the scene-segmentation error measure A2, which depends on the percentage of scenes with measurable error, the agreement of pixel class proportions, the agreement of number of objects for each pixel class and the distance of each misclassified pixel to the nearest pixel of the misclassified class. Results indicate that random and eigenvector feature selection were the poorest methods, manual feature selection somewhat better and error-measure feature selection best. The error-measure feature selection method provides a useful, systematic method of developing and evaluating scene-segmentation algorithms.
---
paper_title: Segmentation evaluation using ultimate measurement accuracy

paper_content:

As a wide range of segmentation techniques have been developed in the last two decades, the evaluation and comparison of segmentation techniques becomes indispensable. In this paper, after a thorough review of previous work, we present a general approach for evaluation and comparison of segmentation techniques. More specifically, under this general framework, we propose to use the ultimate measurement accuracy to assess the performance of different algorithms. In image analysis, the ultimate goals of segmentation and other processing are often to obtain measurements of the object features in the image. Therefore, the accuracy of those ultimate measurements over segmented images would be a good index revealing the performance of segmentation techniques. We feel this measure is of much greater importance than, e.g., error probabilities on pixel labeling, or even specially developed figure of merit. There exist many features describing the properties of the objects in the image. Some of them are discussed here and their applicability and performance in the context of segmentation evaluation are studied. Based on experimental results, we provide some useful guidelines for choosing specific measurements for different evaluation situations and for selecting adequate techniques in particular segmentation applications.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
---
paper_title: Sampling density and quantitative microscopy.

paper_content:

The sampling densities required for the quantitative analysis of digitized microscope images is discussed. It is shown that the Nyquist sampling theorem is not the proper reference point for determining the sampling density when the goal is measurement, although it may be a proper reference point when the goal is image filtering and reconstruction. The problems associated with signal truncation--the use of a finite amount of data--and the finite amount of time available for computation make it impossible to reconstruct an arbitrary image, even if it is bandlimited. Two examples taken from straightforward measurement problems exhibit the fundamental problems associated with the measurement of analog quantities from digital data and the role played by the sampling density.
---
paper_title: Objective and quantitative segmentation evaluation and comparison

paper_content:

Abstract A general framework for segmentation evaluation is introduced after a brief review of previous work. The accuracy of object feature measurement is proposed as a criterion for judging the quality of segmentation results and assessing the performance of applied algorithms. This goal-oriented approach has been shown useful for an objective and quantitative study of segmentation techniques.
---
paper_title: Evaluating quality of compressed medical images : SNR, subjective rating, and diagnostic accuracy : Data compression

paper_content:

Compressing a digital image can facilitate its transmission, storage, and processing. As radiology departments become increasingly digital, the quantities fo their imaging data are forcing consideration of compression in picture archiving and communication systems. Significant compression is achievable anly by lossy algorithms, which do not permit the exact recovery of the original images
---
paper_title: Objective and quantitative segmentation evaluation and comparison

paper_content:

Abstract A general framework for segmentation evaluation is introduced after a brief review of previous work. The accuracy of object feature measurement is proposed as a criterion for judging the quality of segmentation results and assessing the performance of applied algorithms. This goal-oriented approach has been shown useful for an objective and quantitative study of segmentation techniques.
---
paper_title: Image thresholding: Some new techniques

paper_content:

Abstract Some of the existing threshold selection techniques have been critically reviewed. Two algorithms based on a new conditional entropy measure of a partitioned image have been formulated. The approximate minimum error thresholding algorithm of Kittler and Illingworth has been implemented considering the Poisson distribution for the gray level instead of the commonly used normal distribution. Justification in support of the Poisson distribution has also been given. This method is found to be much better both from the point of view of convergence and segmented output. The proposed methods have been applied on a number of images and are found to produce good results. Objective evaluation of the thresholds has been done using divergence, region uniformity, correlation between original image and the segmented image, and second order entropy.
---
paper_title: Three-dimensional image segmentation using a split, merge and group approach

paper_content:

A 3-D segmentation algorithm is presented, based on a split, merge and group approach. It uses a mixed (oct/quad)tree implementation. A number of homogeneity criteria is discussed and evaluated. An example shows the segmentation of mythramycin stained cell nuclei.
---
paper_title: Threshold Evaluation Techniques

paper_content:

Threshold selection techniques have been used as a basic tool in image segmentation, but little work has been done on the problem of evaluating a threshold of an image. The problem of threshold evaluation is addressed, and two methods are proposed for measuring the "goodness" of a thresholded image, one based on a busyness criterion and the other based on a discrepancy or error criterion. These evaluation techniques are applied to a set of infrared images and are shown to be useful in facilitating threshold selection. In fact, both methods usually result in similar or identical thresholds which yield good segmentations of the images.
---
paper_title: Quantitative design and evaluation of enhancement/thresholding edge detectors

paper_content:

Quantitative design and performance evaluation techniques are developed for the enhancement/thresholding class of image edge detectors. The design techniques are based on statistical detection theory and deterministic pattern-recognition classification procedures. The performance evaluation methods developed include: a)deterministic measurement of the edge gradient amplitude; b)comparison of the probabilities of correct and false edge detection; and c) figure of merit computation. The design techniques developed are used to optimally design a variety of small and large mask edge detectors. Theoretical and experimental comparisons of edge detectors are presented.
---
paper_title: Segmentation of microscopic cell scenes.

paper_content:

Different methods for the automated segmentation of microscopic cell scenes are presented with examples. The techniques discussed include edge detection by thresholding, "blob" detection by split-and-merge algorithm, global thresholding using gray-level histograms, hierarchic thresholding using color information, global thresholding using two-dimensional histograms and segmentation by "blob" labeling. Methods are more robust against insignificant changes in the scene and perform more reliably as more a priori knowledge about the scene is incorporated in the segmentation algorithm. The inclusion of both photometric and geometric a priori knowledge can result in a high level of correct segmentations, the cost of which is increased computation time.
---
paper_title: Segmentation evaluation and comparison: a study of various algorithms

paper_content:

ABSTRACT An objective and quantitative study of several representative segmentation algorithms is presented. In this study, themeasurement accuracy of object features from the segmented images is taken tojudge the quality of segmentation results andto assess the performance of applied algorithms. Moreover, some synthetic images are specially generated and used in testexperiments. This evaluation and comparison study reveals the behaviour of those algorithms within various situations,provides their performance ranking under real-like conditions, gives some limits and/or constraints for employing thosealgorithms in different applications as well as indicates several potential directions for improving their performance andinitiating new developments. Since the investigated algorithms are selected from different technique groups, this study alsoshows that the presented approach would be valid and effective for treating a wide range of segmentation algorithms. I. INTRODUCTION Since a large variety of different segmentation algorithms have been developed over last years (see, e.g.,
---
paper_title: Low Level Image Segmentation: An Expert System

paper_content:

A major problem in robotic vision is the segmentation of images of natural scenes in order to understand their content. This paper presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include metarules that embody inferences about the order in which the knowledge rules are matched. They also incorporate focus of attention rules that determine the path of processing within the image. Furthermore, an additional set of higher level rules dynamically alters the processing strategy. This paper discusses the structure and content of the knowledge and control rules for image segmentation.
---
paper_title: A survey of thresholding techniques

paper_content:

Abstract In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.
---
paper_title: Objective and quantitative segmentation evaluation and comparison

paper_content:

Abstract A general framework for segmentation evaluation is introduced after a brief review of previous work. The accuracy of object feature measurement is proposed as a criterion for judging the quality of segmentation results and assessing the performance of applied algorithms. This goal-oriented approach has been shown useful for an objective and quantitative study of segmentation techniques.
---
paper_title: Validation of the interleaved pyramid for the segmentation of 3D vector images

paper_content:

Abstract A multiresolution pyramid with double scale space sampling (compared to the Burt & Hong scheme) for the segmentation of 3D images, of which the elements are multiple valued, is described. Evaluation is carried out by quality constrained cost analysis (QCCA).
---
paper_title: Sampling density and quantitative microscopy.

paper_content:

The sampling densities required for the quantitative analysis of digitized microscope images is discussed. It is shown that the Nyquist sampling theorem is not the proper reference point for determining the sampling density when the goal is measurement, although it may be a proper reference point when the goal is image filtering and reconstruction. The problems associated with signal truncation--the use of a finite amount of data--and the finite amount of time available for computation make it impossible to reconstruct an arbitrary image, even if it is bandlimited. Two examples taken from straightforward measurement problems exhibit the fundamental problems associated with the measurement of analog quantities from digital data and the role played by the sampling density.
---
paper_title: Performance Characterization in Computer Vision

paper_content:

Computer vision algorithms are composed of different sub-algorithms often applied in sequence. Determination of the performance of a total computer vision algorithm is possible if the performance of each of the sub-algorithm constituents is given. The problem, however, is that for most published algorithms, there is no performance characterization which has been established in the research literature. This is an awful state of affairs for the engineers whose job it is to design and build image analysis or machine vision systems.
---
paper_title: Dynamic Measurement of Computer Generated Image Segmentations

paper_content:

This paper introduces a general purpose performance measurement scheme for image segmentation algorithms. Performance parameters that function in real-time distinguish this method from previous approaches that depended on an a priori knowledge of the correct segmentation. A low level, context independent definition of segmentation is used to obtain a set of optimization criteria for evaluating performance. Uniformity within each region and contrast between adjacent regions serve as parameters for region analysis. Contrast across lines and connectivity between them represent measures for line analysis. Texture is depicted by the introduction of focus of attention areas as groups of regions and lines. The performance parameters are then measured separately for each area. The usefulness of this approach lies in the ability to adjust the strategy of a system according to the varying characteristics of different areas. This feedback path provides the means for more efficient and error-free processing. Results from areas with dissimilar properties show a diversity in the measurements that is utilized for dynamic strategy setting.
---
paper_title: Objective and quantitative segmentation evaluation and comparison

paper_content:

Abstract A general framework for segmentation evaluation is introduced after a brief review of previous work. The accuracy of object feature measurement is proposed as a criterion for judging the quality of segmentation results and assessing the performance of applied algorithms. This goal-oriented approach has been shown useful for an objective and quantitative study of segmentation techniques.
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paper_title: Validation of the interleaved pyramid for the segmentation of 3D vector images

paper_content:

Abstract A multiresolution pyramid with double scale space sampling (compared to the Burt & Hong scheme) for the segmentation of 3D images, of which the elements are multiple valued, is described. Evaluation is carried out by quality constrained cost analysis (QCCA).
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paper_title: Performance Characterization in Computer Vision

paper_content:

Computer vision algorithms are composed of different sub-algorithms often applied in sequence. Determination of the performance of a total computer vision algorithm is possible if the performance of each of the sub-algorithm constituents is given. The problem, however, is that for most published algorithms, there is no performance characterization which has been established in the research literature. This is an awful state of affairs for the engineers whose job it is to design and build image analysis or machine vision systems.
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paper_title: Comparison of thresholding techniques using synthetic images and ultimate measurement accuracy

paper_content:

Presents an objective and quantitative study of several thresholding techniques. For this study, three sets of test images to simulate various real situations have been designed. The accuracy of object feature measurement is used to assess the performance. The authors not only provide an insight into these techniques but also show the usefulness of synthetic images and ultimate measurement accuracy in segmentation evaluation and comparison. >
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paper_title: Segmentation evaluation using ultimate measurement accuracy

paper_content:

As a wide range of segmentation techniques have been developed in the last two decades, the evaluation and comparison of segmentation techniques becomes indispensable. In this paper, after a thorough review of previous work, we present a general approach for evaluation and comparison of segmentation techniques. More specifically, under this general framework, we propose to use the ultimate measurement accuracy to assess the performance of different algorithms. In image analysis, the ultimate goals of segmentation and other processing are often to obtain measurements of the object features in the image. Therefore, the accuracy of those ultimate measurements over segmented images would be a good index revealing the performance of segmentation techniques. We feel this measure is of much greater importance than, e.g., error probabilities on pixel labeling, or even specially developed figure of merit. There exist many features describing the properties of the objects in the image. Some of them are discussed here and their applicability and performance in the context of segmentation evaluation are studied. Based on experimental results, we provide some useful guidelines for choosing specific measurements for different evaluation situations and for selecting adequate techniques in particular segmentation applications.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Entropic thresholding using a block source model

paper_content:

Since the pioneer work of Frieden (J. Opt. Soc. Am. 62, 1972, 511-518; Comput. Graphics Image Process. 12, 1980, 40-59), the entropy concept is increasingly used in image analysis, especially in image reconstruction, image segmentation, and image compression. In the present paper a new entropic thresholding method based on a block source model is presented. This new approach is based on a distribution-free local analysis of the image and does not use higher order entropy. Our method is compared to the existing entropic thresholding methods.
---
paper_title: Objective and quantitative segmentation evaluation and comparison

paper_content:

Abstract A general framework for segmentation evaluation is introduced after a brief review of previous work. The accuracy of object feature measurement is proposed as a criterion for judging the quality of segmentation results and assessing the performance of applied algorithms. This goal-oriented approach has been shown useful for an objective and quantitative study of segmentation techniques.
---
paper_title: Validation of the interleaved pyramid for the segmentation of 3D vector images

paper_content:

Abstract A multiresolution pyramid with double scale space sampling (compared to the Burt & Hong scheme) for the segmentation of 3D images, of which the elements are multiple valued, is described. Evaluation is carried out by quality constrained cost analysis (QCCA).
---
paper_title: Task-directed evaluation of image segmentation methods

paper_content:

In the image processing literature many methods to segment 2D and 3D images have been presented. However, relatively little effort has been spent on the validation of the results of these methods. The goal of the paper is to explore a validation methodology that is based on developing a task-directed quality norm that can be used as a constraint in cost analysis. In this methodology a segmentation method is evaluated by the cost reduction it provides relative to the cost of a full-interactive (manual) segmentation. This cost is constrained by a quality threshold, so that less-than-perfect segmentations are allowed. In this way segmentation methods can be compared, which are designed for the same task, but are different of nature. >
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paper_title: Image thresholding: Some new techniques

paper_content:

Abstract Some of the existing threshold selection techniques have been critically reviewed. Two algorithms based on a new conditional entropy measure of a partitioned image have been formulated. The approximate minimum error thresholding algorithm of Kittler and Illingworth has been implemented considering the Poisson distribution for the gray level instead of the commonly used normal distribution. Justification in support of the Poisson distribution has also been given. This method is found to be much better both from the point of view of convergence and segmented output. The proposed methods have been applied on a number of images and are found to produce good results. Objective evaluation of the thresholds has been done using divergence, region uniformity, correlation between original image and the segmented image, and second order entropy.
---
paper_title: Computational Techniques in the Visual Segmentation of Static Scenes.

paper_content:

A wide range of segmentation techniques continues to evolve in the literature on scene analysis. Many of these approaches have been constrained to limited applications or goals. This survey analyzes the complexities encountered in applying these techniques to color images of natural scenes involving complex textured objects. It also explores new ways of using the techniques to overcome some of the problems which are described. An outline of considerations in the development of a general image segmentation system which can provide input to a semantic interpretation process is distributed throughout the paper. In particular, the problems of feature selection and extraction in images with textural variations are discussed. The approaches to segmentation are divided into two broad categories, boundary formation and region formation. The tools for extraction of boundaries involve spatial differentiation, nonmaxima suppression, relaxation processes, and grouping of local edges into segments. Approaches to region formation include region growing under local spatial guidance, histograms for analysis of global feature activity, and finally an integration of the strengths of each by a spatial analysis of feature activity. A brief discussion of attempts by others to integrate the segmentation and interpretation phrases is also provided. The discussion is supported by a variety of experimental results.
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paper_title: A survey of threshold selection techniques

paper_content:

Abstract The use of thresholding as a tool in image segmentation has been extensively studied, and a variety of techniques have been proposed for automatic threshold selection. This paper presents a review of these techniques, including global, local, and dynamic methods.
---
paper_title: Entropic thresholding using a block source model

paper_content:

Since the pioneer work of Frieden (J. Opt. Soc. Am. 62, 1972, 511-518; Comput. Graphics Image Process. 12, 1980, 40-59), the entropy concept is increasingly used in image analysis, especially in image reconstruction, image segmentation, and image compression. In the present paper a new entropic thresholding method based on a block source model is presented. This new approach is based on a distribution-free local analysis of the image and does not use higher order entropy. Our method is compared to the existing entropic thresholding methods.
---
paper_title: Dynamic Measurement of Computer Generated Image Segmentations

paper_content:

This paper introduces a general purpose performance measurement scheme for image segmentation algorithms. Performance parameters that function in real-time distinguish this method from previous approaches that depended on an a priori knowledge of the correct segmentation. A low level, context independent definition of segmentation is used to obtain a set of optimization criteria for evaluating performance. Uniformity within each region and contrast between adjacent regions serve as parameters for region analysis. Contrast across lines and connectivity between them represent measures for line analysis. Texture is depicted by the introduction of focus of attention areas as groups of regions and lines. The performance parameters are then measured separately for each area. The usefulness of this approach lies in the ability to adjust the strategy of a system according to the varying characteristics of different areas. This feedback path provides the means for more efficient and error-free processing. Results from areas with dissimilar properties show a diversity in the measurements that is utilized for dynamic strategy setting.
---
paper_title: Sampling density and quantitative microscopy.

paper_content:

The sampling densities required for the quantitative analysis of digitized microscope images is discussed. It is shown that the Nyquist sampling theorem is not the proper reference point for determining the sampling density when the goal is measurement, although it may be a proper reference point when the goal is image filtering and reconstruction. The problems associated with signal truncation--the use of a finite amount of data--and the finite amount of time available for computation make it impossible to reconstruct an arbitrary image, even if it is bandlimited. Two examples taken from straightforward measurement problems exhibit the fundamental problems associated with the measurement of analog quantities from digital data and the role played by the sampling density.
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paper_title: Comments on gray-level thresholding of images using a correlation criterion

paper_content:

In our comments we deal with two automatic thresholding algorithms proposed by Otsu and Brink, respectively. These algorithms, in spite of their different approach, lead to one and the same function to be maximized.
---
paper_title: Image thresholding: Some new techniques

paper_content:

Abstract Some of the existing threshold selection techniques have been critically reviewed. Two algorithms based on a new conditional entropy measure of a partitioned image have been formulated. The approximate minimum error thresholding algorithm of Kittler and Illingworth has been implemented considering the Poisson distribution for the gray level instead of the commonly used normal distribution. Justification in support of the Poisson distribution has also been given. This method is found to be much better both from the point of view of convergence and segmented output. The proposed methods have been applied on a number of images and are found to produce good results. Objective evaluation of the thresholds has been done using divergence, region uniformity, correlation between original image and the segmented image, and second order entropy.
---
paper_title: Image segmentation and image models

paper_content:

This paper discusses image segmentation techniques from the standpoint of the assumptions that an image should satisfy in order for a particular technique to be applicable to it. These assumptions, which are often not stated explicitly, can be regarded as (perhaps informal) "models" for classes of images. The paper emphasizes two basic classes of models: statistical models that describe the pixel population in an image or region, and spatial models that describe the decomposition of an image into regions.
---
paper_title: Dynamic Measurement of Computer Generated Image Segmentations

paper_content:

This paper introduces a general purpose performance measurement scheme for image segmentation algorithms. Performance parameters that function in real-time distinguish this method from previous approaches that depended on an a priori knowledge of the correct segmentation. A low level, context independent definition of segmentation is used to obtain a set of optimization criteria for evaluating performance. Uniformity within each region and contrast between adjacent regions serve as parameters for region analysis. Contrast across lines and connectivity between them represent measures for line analysis. Texture is depicted by the introduction of focus of attention areas as groups of regions and lines. The performance parameters are then measured separately for each area. The usefulness of this approach lies in the ability to adjust the strategy of a system according to the varying characteristics of different areas. This feedback path provides the means for more efficient and error-free processing. Results from areas with dissimilar properties show a diversity in the measurements that is utilized for dynamic strategy setting.
---
paper_title: Three-dimensional image segmentation using a split, merge and group approach

paper_content:

A 3-D segmentation algorithm is presented, based on a split, merge and group approach. It uses a mixed (oct/quad)tree implementation. A number of homogeneity criteria is discussed and evaluated. An example shows the segmentation of mythramycin stained cell nuclei.
---
paper_title: Image segmentation as an estimation problem

paper_content:

Abstract Picture segmentation is expressed as a sequence of decision problems with the framework of a split-and-merge algorithm. First regions of an arbitrary initial segmentation are tested for uniformity and if not uniform they are subdivided into smaller regions, or set aside if their size is below a given threshold. Next regions classified as uniform are subject to a cluster analysis to identify similar types which are merged. At this point there exist reliable estimates of the parameters of the random field of each type of region and they are used to classify some of the remaining small regions. Any regions remaining after this step are considered part of a boundary ambiguity zone. The location of the boundary is estimated then by interpolation between the existing uniform regions. Experimental results on artificial pictures are also included.
---
paper_title: Objective and quantitative segmentation evaluation and comparison

paper_content:

Abstract A general framework for segmentation evaluation is introduced after a brief review of previous work. The accuracy of object feature measurement is proposed as a criterion for judging the quality of segmentation results and assessing the performance of applied algorithms. This goal-oriented approach has been shown useful for an objective and quantitative study of segmentation techniques.
---
paper_title: A survey of thresholding techniques

paper_content:

Abstract In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.
---
paper_title: Image thresholding: Some new techniques

paper_content:

Abstract Some of the existing threshold selection techniques have been critically reviewed. Two algorithms based on a new conditional entropy measure of a partitioned image have been formulated. The approximate minimum error thresholding algorithm of Kittler and Illingworth has been implemented considering the Poisson distribution for the gray level instead of the commonly used normal distribution. Justification in support of the Poisson distribution has also been given. This method is found to be much better both from the point of view of convergence and segmented output. The proposed methods have been applied on a number of images and are found to produce good results. Objective evaluation of the thresholds has been done using divergence, region uniformity, correlation between original image and the segmented image, and second order entropy.
---
paper_title: Error measures for objective assessment of scene segmentation algorithms.

paper_content:

Scene segmentation is an important element in pattern recognition problems. Previous efforts to evaluate and compare scene segmentation procedures have been largely subjective. Quantitative error measures would facilitate objective comparison of scene segmentation algorithms. A theoretical discussion leading to a new generalized quantitative error measure, G2, based on comparison of both pixel class proportions and spatial distributions of "true" and test segmentations, is presented. This error measure was tested on 14 manual segmentations and 40 gynecologic cytology specimens segmented with five different scene segmentation techniques. Results indicate that G2 seems to have the desirable properties of correlation with human observation, categorization of error allowing for weighting, invariance with picture size and ease of computation necessary for a useful scene segmentation error measure.
---
paper_title: Segmentation evaluation and comparison: a study of various algorithms

paper_content:

ABSTRACT An objective and quantitative study of several representative segmentation algorithms is presented. In this study, themeasurement accuracy of object features from the segmented images is taken tojudge the quality of segmentation results andto assess the performance of applied algorithms. Moreover, some synthetic images are specially generated and used in testexperiments. This evaluation and comparison study reveals the behaviour of those algorithms within various situations,provides their performance ranking under real-like conditions, gives some limits and/or constraints for employing thosealgorithms in different applications as well as indicates several potential directions for improving their performance andinitiating new developments. Since the investigated algorithms are selected from different technique groups, this study alsoshows that the presented approach would be valid and effective for treating a wide range of segmentation algorithms. I. INTRODUCTION Since a large variety of different segmentation algorithms have been developed over last years (see, e.g.,
---
paper_title: Low Level Image Segmentation: An Expert System

paper_content:

A major problem in robotic vision is the segmentation of images of natural scenes in order to understand their content. This paper presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include metarules that embody inferences about the order in which the knowledge rules are matched. They also incorporate focus of attention rules that determine the path of processing within the image. Furthermore, an additional set of higher level rules dynamically alters the processing strategy. This paper discusses the structure and content of the knowledge and control rules for image segmentation.
---
paper_title: Error measures for scene segmentation

paper_content:

Abstract Scene segmentation is an important problem in pattern recognition. Current subjective methods for evaluation and comparison of scene segmentation techniques are inadequate and objective quantitative measures are desirable. Two error measures, the percentage area misclassified ( p ) and a new pixel distance error (ϵ) were defined and evaluated in terms of their correlation with human observation for comparison of multiple segmentations of the same scene and multiple scenes segmented by the same technique. The results indicate that both these measures can be helpful in the evaluation and comparison of scene segmentation procedures.
---
paper_title: Three-dimensional image segmentation using a split, merge and group approach

paper_content:

A 3-D segmentation algorithm is presented, based on a split, merge and group approach. It uses a mixed (oct/quad)tree implementation. A number of homogeneity criteria is discussed and evaluated. An example shows the segmentation of mythramycin stained cell nuclei.
---
paper_title: Scene-segmentation algorithm development using error measures.

paper_content:

Development of scene-segmentation algorithms has generally been an ad hoc process. This paper presents a systematic technique for developing these algorithms using error-measure minimization. If scene segmentation is regarded as a problem of pixel classification whereby each pixel of a scene is assigned to a particular object class, development of a scene-segmentation algorithm becomes primarily a process of feature selection. In this study, four methods of feature selection were used to develop segmentation techniques for cervical cytology images: (1) random selection, (2) manual selection (best features in the subjective judgment of the investigator), (3) eigenvector selection (ranking features according to the largest contribution to each eigenvector of the feature covariance matrix) and (4) selection using the scene-segmentation error measure A2. Four features were selected by each method from a universe of 35 features consisting of gray level, color, texture and special pixel neighborhood features in 40 cervical cytology images . Evaluation of the results was done with a composite of the scene-segmentation error measure A2, which depends on the percentage of scenes with measurable error, the agreement of pixel class proportions, the agreement of number of objects for each pixel class and the distance of each misclassified pixel to the nearest pixel of the misclassified class. Results indicate that random and eigenvector feature selection were the poorest methods, manual feature selection somewhat better and error-measure feature selection best. The error-measure feature selection method provides a useful, systematic method of developing and evaluating scene-segmentation algorithms.
---

Title: A Survey on Evaluation Methods for Image Segmentation

Section 1: INTRODUCTION
Description 1: Write about the significance of image segmentation, the need for evaluation methods, and provide an overview of the paper's purpose and structure.

Section 2: ANALYTICAL METHODS
Description 2: Discuss the analytical methods used for evaluating segmentation algorithms, highlighting their principles, advantages, and limitations.

Section 3: EMPIRICAL GOODNESS METHODS
Description 3: Describe the empirical goodness methods that evaluate segmentation algorithms based on the quality of segmented images using various goodness measures.

Section 4: EMPIRICAL DISCREPANCY METHODS
Description 4: Explain empirical discrepancy methods that use reference images to measure the disparity between segmented results and the ideal segmentation to evaluate the performance of algorithms.

Section 5: COMPARISON OF METHOD GROUPS
Description 5: Compare the analytical methods, empirical goodness methods, and empirical discrepancy methods, discussing their generality, quantitative and objective nature, complexity, and application considerations.

Section 6: COMPARISON OF SOME EMPIRICAL METHODS
Description 6: Present an experimental comparison of several commonly used empirical methods, detailing their performance and rankings.

Section 7: SPECIAL EVALUATION METHODS
Description 7: Discuss special evaluation methods that do not fit neatly into the previous categories, describing how they work and their unique aspects.

Section 8: COMMON PROBLEMS FOR MOST EXISTING METHODS
Description 8: Highlight common issues associated with existing evaluation methods, such as bias in criteria and the influence of subjective factors.

Section 9: CONCLUDING REMARKS
Description 9: Summarize the findings of the survey, emphasizing the need for continued research in segmentation evaluation and proposing future directions.

Section 10: ACKNOWLEDGEMENT
Description 10: Acknowledge contributions and suggestions from reviewers and collaborators.