Medical Image Analysis Using Soft Computing Techniques
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Soft computing methodologies have gained increasing attention over the past years due to their suitability for problem solving in the processing and evaluation of medical data. The processing of medical data includes two major processes called as segmentation and classification. Image segmentation is the process in which a single image is partitioned into several groups based on similarity measures. Image classification is the process in which several images are categorized into several groups. Image segmentation techniques are normally used for volumetric analysis of abnormalities in medical images and classification techniques are used for identification of the nature of disease. In both cases, the accuracy and convergence rate of the methodologies used must be significantly positive. Hence, soft computing techniques are widely preferred for such applications. In this chapter, the application of few soft computing techniques such as Fuzzy C-Means (FCM), K-means and Support Vector Machine (SVM) for image segmentation and classification are explored. Brain image database and Retinal image database are used in these experiments. The approaches are analyzed in terms of some performance measures and found to be more suitable for medical applications.
KeywordsMR images Classification Segmentation Computing
The authors wish to thank M/s. Devaki Scan Centre for their help regarding database and validation.
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