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Analysis of CT brain images using radial basis function neural network
, R. Ganesan
Published in Defense Scientific Information and Documentation Centre
Volume: 62
Issue: 4
Pages: 212 - 218
Medical image processing and analysis is the tool to assist radiologists in the diagnosis process to obtain a more accurate and faster diagnosis. In this work, we have developed a neural network to classify the computer tomography (CT) brain tumor image for automatic diagnosis. This system is divided into four steps namely enhancement, segmentation, feature extraction and classifcation. In the frst phase, an edge-based selective median flter is used to improve the visibility of the loss of the gray-white matter interface in CT brain tumor images. Second phase uses a modifed version of shift genetic algorithm for the segmentation. Next phase extracts the textural features using statistical texture analysis method. These features are fed into classifers like BPN, Fuzzy k-NN, and radial basis function network. The performances of these classifers are analyzed in the fnal phase with receiver operating characteristic and precision-recall curve. The result shows that the CAD system is only to develop the tool for brain tumor and proposed method is very accurate and computationally more effcient and less time consuming. © 2012, DESIDOC.
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JournalDefence Science Journal
PublisherDefense Scientific Information and Documentation Centre