A REVIEW ON THE CURRENT SEGMENTATION ALGORITHMS FOR MEDICAL IMAGES
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paper_title: Seeded region growing

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We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures. >
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paper_title: Segmentation of medical images using adaptive region growing

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Interaction increases flexibility of segmentation but it leads to undesirable behavior of an algorithm if knowledge being requested is inappropriate. In region growing, this is the case for defining the homogeneity criterion as its specification depends also on image formation properties that are not known to the user. We developed a region growing algorithm that learns its homogeneity criterion automatically from characteristics of the region to be segmented. The method is based on a model that describes homogeneity and simple shape properties of the region. Parameters of the homogeneity criterion are estimated from sample locations in the region. These locations are selected sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. The method was tested for segmentation on test images and of structures in CT images. We found the method to work reliable if the model assumption on homogeneity and region characteristics are true. Furthermore, the model is simple but robust, thus allowing for a certain degree of deviation from model constraints and still delivering the expected segmentation result. This approach was extended to a fully automatic and complete segmentation method by using the pixels with the smallest gradient length in the not yet segmented image region as a seed point.
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paper_title: Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm

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We propose a methodology that incorporates k-means and improved watershed segmentation algorithm for medical image segmentation. The use of the conventional watershed algorithm for medical image analysis is widespread because of its advantages, such as always being able to produce a complete division of the image. However, its drawbacks include over-segmentation and sensitivity to false edges. We address the drawbacks of the conventional watershed algorithm when it is applied to medical images by using k-means clustering to produce a primary segmentation of the image before we apply our improved watershed segmentation algorithm to it. The k-means clustering is an unsupervised learning algorithm, while the improved watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map and post-segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. By comparing the number of partitions in the segmentation maps of 50 images, we showed that our proposed methodology produced segmentation maps which have 92% fewer partitions than the segmentation maps produced by the conventional watershed algorithm
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paper_title: Neural network based segmentation of magnetic resonance images of the brain

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Presents a study investigating the potential of artificial neural networks (ANN's) for the classification and segmentation of magnetic resonance (MR) images of the human brain. In this study, the authors present the application of a learning vector quantization (LVQ) ANN for the multispectral supervised classification of MR images. The authors have modified the LVQ for better and more accurate classification. They have compared the results using LVQ ANN versus back-propagation ANN. This comparison shows that, unlike back-propagation ANN, the authors' method is insensitive to the gray-level variation of MR images between different slices. It shows that tissue segmentation using LVQ ANN also performs better and faster than that using back-propagation ANN.
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paper_title: On Active Contour Models and Balloons

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Abstract The use of energy-minimizing curves, known as “snakes,” to extract features of interest in images has been introduced by Kass, Witkin & Terzopoulos ( Int. J. Comput. Vision 1, 1987, 321–331). We present a model of deformation which solves some of the problems encountered with the original method. The external forces that push the curve to the edges are modified to give more stable results. The original snake, when it is not close enough to contours, is not attracted by them and straightens to a line. Our model makes the curve behave like a balloon which is inflated by an additional force. The initial curve need no longer be close to the solution to converge. The curve passes over weak edges and is stopped only if the edge is strong. We give examples of extracting a ventricle in medical images. We have also made a first step toward 3D object reconstruction, by tracking the extracted contour on a series of successive cross sections.
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paper_title: Segmentation of dynamic PET images using cluster analysis

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Quantitative positron emission tomography (PET) studies provide in vivo measurements of dynamic physiological and biochemical processes in humans. A limitation of PET is an inability to provide precise anatomic localization due to relatively poor spatial resolution when compared to magnetic resonance (MR) imaging. Manual placement of region-of-interest (ROI) is commonly used in clinical and research settings in analysis of PET datasets. However, this approach is operator dependent and time-consuming. A semi- or fully-automated ROI delineation (or segmentation) method offers advantages by reducing operator error/subjectivity and thereby improving reproducibility. In this work, we describe an approach to automatically segment dynamic PET images using cluster analysis and we validate our approach with a simulated phantom study and assess its performance with real dynamic PET data. Our preliminary results suggest that cluster analysis can automatically segment tissues in dynamic PET studies and has the potential to replace manual ROI delineation for some applications.
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paper_title: The application of competitive Hopfield neural network to medical image segmentation

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In this paper, a parallel and unsupervised approach using the competitive Hopfield neural network (CHNN) is proposed for medical image segmentation. It is a kind of Hopfield network which incorporates the winner-takes-all (WTA) learning mechanism. The image segmentation is conceptually formulated as a problem of pixel clustering based upon the global information of the gray level distribution. Thus, the energy function for minimization is defined as the mean of the squared distance measures of the gray levels within each class. The proposed network avoids the onerous procedure of determining values for the weighting factors in the energy function. In addition, its training scheme enables the network to learn rapidly and effectively. For an image of n gray levels and c interesting objects, the proposed CHNN would consist of n by c neurons and be independent of the image size. In both simulation studies and practical medical image segmentation, the CHNN method shows promising results in comparison with two well-known methods: the hard and the fuzzy c-means (FCM) methods.
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paper_title: Shape deformation: SVM regression and application to medical image segmentation

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This paper presents a novel landmark-based shape deformation method. This method effectively solves two problems inherent in landmark-based shape deformation: (a) identification of landmark points from a given input image, and (b) regularized deformation the shape of an an object defined in a template. The second problem is solved using a new constrained support vector machine (SVM) regression technique, in which a thin-plate kernel is utilized to provide non-rigid shape deformations. This method offers several advantages over existing landmark-based methods. First, it has a unique capability to detect and use multiple candidate landmark points in an input image to improve landmark detection. Second, it can handle the case of missing landmarks, which often arises in dealing with occluded images. We have applied the proposed method to extract the scalp contours from brain cryosection images with very encouraging results.
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paper_title: Atlas Guided Identification of Brain Structures by Combining 3D Segmentation and SVM Classification

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This study presents a novel automatic approach for the identification of anatomical brain structures in magnetic resonance images (MRI). The method combines a fast multiscale multi-channel three dimensional (3D) segmentation algorithm providing a rich feature vocabulary together with a support vector machine (SVM) based classifier. The segmentation produces a full hierarchy of segments, expressed by an irregular pyramid with only linear time complexity. The pyramid provides a rich, adaptive representation of the image, enabling detection of various anatomical structures at different scales. A key aspect of the approach is the thorough set of multiscale measures employed throughout the segmentation process which are also provided at its end for clinical analysis. These features include in particular the prior probability knowledge of anatomic structures due to the use of an MRI probabilistic atlas. An SVM classifier is trained based on this set of features to identify the brain structures. We validated the approach using a gold standard real brain MRI data set. Comparison of the results with existing algorithms displays the promise of our approach.
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paper_title: Modified fuzzy c-mean in medical image segmentation

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This paper describes the application of fuzzy set theory in medical imaging, namely the segmentation of brain images. We propose a fully automatic technique to obtain image clusters. A modified fuzzy c-mean (FCM) classification algorithm is used to provide a fuzzy partition. Our new method, inspired from the Markov Random Field (MRF), is less sensitive to noise as it filters the image while clustering it, and the filter parameters are enhanced in each iteration by the clustering process. We applied the new method on a noisy CT scan and on a single channel MRI scan. We recommend using a methodology of over segmentation to the textured MRI scan and a user guided-interface to obtain the final clusters. One of the applications of this technique is TBI recovery prediction in which it is important to consider the partial volume. It is shown that the system stabilizes after a number of iterations with the membership value of the region contours reflecting the partial volume value. The final stage of the process is devoted to decision making or the defuzzification process.
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paper_title: Atlas-guided segmentation of brain images via optimizing neural networks

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Automated segmentation of magnetic resonance (MR) brain imagery into anatomical regions is a complex task that appears to need contextual guidance in order to overcome problems associated with noise, missing data, and the overlap of features associated with different anatomical regions. In this work, the contextual information is provided in the form of an anatomical brain atlas. The atlas provides defaults that supplement the low-level MR image data and guide its segmentation. The matching of atlas to image data is represented by a set of deformable contours that seek compromise fits between expected model information and image data. The dynamics that deform the contours solves both a correspondence problem (which element of the deformable contour corresponds to which elements of the atlas and image data?) and a fitting problem (what is the optimal contour that corresponds to a compromise of atlas and image data while maintaining smoothness?). Some initial results on simple 2D contours are shown.
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paper_title: A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis

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This paper presents a physics-based approach to anatomical surface segmentation, reconstruction, and tracking in multidimensional medical images. The approach makes use of a dynamic "balloon" model--a spherical thin-plate under tension surface spline which deforms elastically to fit the image data. The fitting process is mediated by internal forces stemming from the elastic properties of the spline and external forces which are produced form the data. The forces interact in accordance with Lagrangian equations of motion that adjust the model's deformational degrees of freedom to fit the data. We employ the finite element method to represent the continuous surface in the form of weighted sums of local polynomial basis functions. We use a quintic triangular finite element whose nodal variables include positions as well as the first and second partial derivatives of the surface. We describe a system, implemented on a high performance graphics workstation, which applies the model fitting technique to the segmentation of the cardiac LV surface in volume (3D) CT images and LV tracking in dynamic volume (4D) CT images to estimate its nonrigid motion over the cardiac cycle. The system features a graphical user interface which minimizes error by affording specialist users interactive control over the dynamic model fitting process.
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paper_title: Topology-independent shape modeling scheme

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Shape modeling is an important constituent of computer vision as well as computer graphics research. Shape models aid the tasks of object representation and recognition. This dissertation presents a new approach to shape modeling which retains the most attractive features of existing methods, and overcomes their prominent limitations. Our technique can be applied to model arbitrarily complex shapes, which include shapes with significant protrusions, and to situations where no a priori assumption about the object's topology is made. A single instance of our model, when presented with an image having more than one object of interest, has the ability to split freely to represent each object. This method is based on the ideas developed by Osher and Sethian to model propagating solid/liquid interfaces with curvature-dependent speeds. The interface (front) is a closed, nonintersecting, hypersurface flowing along its gradient field with constant speed or a speed that depends on the curvature. It is moved by solving a "Hamilton-Jacobi" type equation written for a function in which the interface is a particular level set. A speed term synthesized from the image is used to stop the interface in the vicinity of the object boundaries. The resulting equation of motion is solved by employing entropy-satisfying upwind finite difference schemes. We also introduce a new algorithm for rapid advancement of the front using what we call a narrow-band updation scheme. This leads to significant improvement in the time complexity of the shape recovery procedure in 2D. An added advantage of our modeling scheme is that it can easily be extended to any number of space dimensions. The efficacy of the scheme is demonstrated with numerical experiments on low contrast medical images. We also demonstrate the recovery of 3D shapes.
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paper_title: Snakes , Shapes , and Gradient Vector Flow

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Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.
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paper_title: Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations

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We devise new numerical algorithms, called PSC algorithms, for following fronts propagating with curvature-dependent speed. The speed may be an arbitrary function of curvature, and the front also can be passively advected by an underlying flow. These algorithms approximate the equations of motion, which resemble Hamilton-Jacobi equations with parabolic right-hand sides, by using techniques from hyperbolic conservation laws. Non-oscillatory schemes of various orders of accuracy are used to solve the equations, providing methods that accurately capture the formation of sharp gradients and cusps in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be also used for more general Hamilton-Jacobi-type problems. We demonstrate our algorithms by computing the solution to a variety of surface motion problems.
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paper_title: On Active Contour Models and Balloons

paper_content:

Abstract The use of energy-minimizing curves, known as “snakes,” to extract features of interest in images has been introduced by Kass, Witkin & Terzopoulos ( Int. J. Comput. Vision 1, 1987, 321–331). We present a model of deformation which solves some of the problems encountered with the original method. The external forces that push the curve to the edges are modified to give more stable results. The original snake, when it is not close enough to contours, is not attracted by them and straightens to a line. Our model makes the curve behave like a balloon which is inflated by an additional force. The initial curve need no longer be close to the solution to converge. The curve passes over weak edges and is stopped only if the edge is strong. We give examples of extracting a ventricle in medical images. We have also made a first step toward 3D object reconstruction, by tracking the extracted contour on a series of successive cross sections.
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paper_title: Snakes: Active contour models

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A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.
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paper_title: Geodesic Active Contours

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A novel scheme for the detection of object boundaries is presented. The technique is based on active contours deforming according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. The minimal distance curve lays in a Riemannian space whose metric as defined by the image content. This geodesic approach for object segmentation allows to connect classical "snakes" based on energy minimization and geometric active contours based on the theory of curve evolution. Previous models of geometric active contours are improved as showed by a number of examples. Formal results concerning existence, uniqueness, stability, and correctness of the evolution are presented as well. >
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Title: A REVIEW ON THE CURRENT SEGMENTATION ALGORITHMS FOR MEDICAL IMAGES

Section 1: INTRODUCTION
Description 1: Provide an overview of imaging techniques like CT and MRI, the significance of segmentation in medical imaging, challenges, and an outline of the paper structure.

Section 2: Algorithms Based on Threshold
Description 2: Discuss the main ideas and types of threshold-based segmentation algorithms, including edge-based, region-based, and hybrid algorithms, along with their strengths and weaknesses.

Section 3: Algorithms Based on Pattern Recognition Techniques
Description 3: Review supervised and unsupervised classification algorithms used in medical image segmentation, such as artificial neural networks, support vector machines, and clustering algorithms, and highlight their applications and limitations.

Section 4: Algorithms Based on Deformable Models
Description 4: Explore parametric and geometric deformable models, their underlying methodologies, advantages, and applications in complex medical image segmentations.

Section 5: DISCUSSION
Description 5: Summarize the comparative advantages and disadvantages of the different categories of segmentation algorithms, discussing their potential applications and constraints in medical imaging.

Section 6: CONCLUSION
Description 6: Conclude with a summary of the main points discussed in the paper, the implications of combining multiple segmentation techniques, and considerations for designing effective segmentation algorithms based on specific medical imaging tasks.