Tumor, Edema and Atrophy Segmentation of Brain MRI with Wavelet Transform and Semantic Features

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MRI Brain Image Segmentation is one of the difficult and complex techniques in the medical field. Normally the pathological tissues such as Tumor and Edema are easily segmented. In this paper, both the normal tissues such as WM (White Matter), GM (Gray Matter) and CSF (Cerebrospinal Fluid) and the pathological tissues such as Tumor, Edema and also Atrophy in the MRI Brain Images are segmented effectively. Initially, the Wavelet Transform features and the Semantic feature from the MRI Brain Images are extracted in two different ways. These extracted features are the input to the next process. Then the proposed segmentation technique performs classification process by utilizing a dual Artificial Neural Network. The ANN is helpful for classifying whether the image is normal or abnormal. Based on the results, the segmentation is carried out. In Segmentation, the normal tissues such as WM, GM and CSF are segmented from the normal MRI images and pathological tissues such as Tumor, Edema and Atrophy are segmented from the abnormal images. The implementation result shows the efficiency of proposed tissue segmentation technique in segmenting the tissues accurately from the MRI images. The performance of the segmentation technique is evaluated by performance measures such as accuracy, specificity and sensitivity.
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Segmentation; Pathological tissues; Artificial Neural Network; White Matter; Gray Matter; Cerebrospinal Fluid; Tumor; Edema; Atrophy

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