Medical imaging fusion applications: An overview
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paper_title: Adaptive neural network imaging in medical systems

paper_content:

Recent technological advances in medicine has facilitated the development of sophisticated equipment enabling the better delivery of health care services. In parallel, artificial neural networks have emerged as promising tools for the application and implementation of intelligent systems. The aim of this paper is to provide a snapshot of the application of neural network systems in medical imaging. The paper highlights neural network applications in the analysis of cervicovaginal smears, mammography, microscopy, ultrasound imaging, and lesion placement in pallidotomy. It is anticipated that the application of neural network systems in medicine will provide the framework for the development of emerging medical systems, enabling the better delivery of health care.
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paper_title: Some terms of reference in data fusion

paper_content:

The concept of data fusion is easy to understand. H owever its exact meaning varies from one scientist to another. A working group, set up by the European Association of Remote Sensing Laboratories (EARSeL) and the French Society for Electricity and Electronics (SEE, French affiliate of the IEEE), de voted most of its efforts to establish a lexicon or terms of reference, which is presented in this communication. A new definition o f the data fusion is proposed which better fits the remote sensing domain. Data fusion should be seen as a framework, not merely as a collection of tools and means. This definition e mphasizes the concepts and the fundamentals in remote sensing. The establishment o f a lexicon or terms of reference allows the scient ific community to express the same ideas using the same words, and also to dissem inate their knowledge towards the industry and 'cus tomers' communities. Moreover it is a sine qua non condition to set up clearly the concept of data fu sion and the associated formal framework. Such a framework is mandatory for a better understanding o f data fusion fundamentals and of its properties. I t allows a better description and formalization of the potentials of synergy betw een the remote sensing data, and accordingly, a bet ter exploitation of these data. Finally, the introduction of the concept of data fu sion into the remote sensing domain should raise th e awareness of our colleagues on the whole chain ranging from the sensor to the d ecision, including the management, assessment and control of the quality of the information. The problem of alignment of the inform ation to be fused is very difficult to tackle. It i s a pre-requisite to any fusion process and should be considered with great care.
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paper_title: Adaptive neural network imaging in medical systems

paper_content:

Recent technological advances in medicine has facilitated the development of sophisticated equipment enabling the better delivery of health care services. In parallel, artificial neural networks have emerged as promising tools for the application and implementation of intelligent systems. The aim of this paper is to provide a snapshot of the application of neural network systems in medical imaging. The paper highlights neural network applications in the analysis of cervicovaginal smears, mammography, microscopy, ultrasound imaging, and lesion placement in pallidotomy. It is anticipated that the application of neural network systems in medicine will provide the framework for the development of emerging medical systems, enabling the better delivery of health care.
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paper_title: Fusion of ultrasonic and radiographic images of the breast

paper_content:

The diagnosis of breast pathologies requires in most cases a mammographic and an echographic examination. Radiographic and ultrasonic images are complementary for the elaboration of a diagnosis. To realize both examinations quasi simultaneously, a device has been developed to acquire data from the same volume of interest in the breast: 2D radiographic images and a series of ultrasonic images which constitutes a volume of data. From this volume, an ultrasonic image, similar to a X-ray image is synthesized by a conical projection of the volume. The important information provided by each modality is then merged by data fusion techniques, involving fuzzy logic, to provide an image which is a tool for diagnostic aid.
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paper_title: Automatic assessment of myocardial viability based on PET-MRI data fusion

paper_content:

In this paper, a fusion system which combines data generated by tagged magnetic resonance imaging (MRI) and F18-FDG positron emission tomography (PET) is presented. MRI and PET complementarity leads to a very accurate assessment of the myocardial viability in patients with coronary heart disease. An accurate viability analysis allows a better prediction of the successes of the revascularization procedure. The fusion system is based on soft computing techniques. It is a modular network which consists of four adaptive network-based fuzzy inference systems (ANFIS) organized in a hierarchical way. The network is able to learn and adapt itself and integrates expert knowledge. It is considered a valuable tool for clinical and research applications.
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Title: Medical Imaging Fusion Applications: An Overview

Section 1: Introduction
Description 1: Present the overall objective of Computer Aided Diagnostic (CAD) systems and introduce the concept of medical imaging fusion, highlighting its benefits and applications.

Section 2: Literature Review and a Snapshot of Selected Applications
Description 2: Summarize the literature search results on data fusion in medical imaging, citing relevant statistics and providing examples of its application in various medical fields.

Section 3: Case Studies
Description 3: Discuss detailed case studies showcasing image fusion techniques in oncology, microscopy, ultrasound imaging, and lesion placement in pallidotomy.

Section 4: A Modular Neural Network System for the Analysis of Nuclei in Histopathological Sections
Description 4: Describe the modular neural network system used for detecting and classifying breast cancer nuclei, detailing the methods employed and the results obtained.

Section 5: A Multi-feature Multi-classifier System for the Classification of Atherosclerotic Carotid Plaques
Description 5: Explain the computer-aided system developed for characterizing carotid plaques using texture features, and discuss its effectiveness in identifying patients at risk of stroke.

Section 6: Concluding Remarks
Description 6: Summarize the concluding remarks, discussing the general issues in medical imaging and data fusion, and providing insights and recommendations for future research.