Analysis of CT Brain images using Radial Basis Function Neural Network

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 classification. In the first phase, an edge-based selective median filter is used to improve the visibility of the loss of the gray-white matter interface in CT brain tumor images. Second phase uses a modified version of shift genetic algorithm for the segmentation. Next phase extracts the textural features using statistical texture analysis method. These features are fed into classifiers like BPN, Fuzzy k-NN, and radial basis function network. The performances of these classifiers are analyzed in the final 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 efficient and less time consuming.