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Medical images are widely used by the physicians to find abnormalities in human bodies. However the images sometimes are corrupted with a noise which normally exist or occurs during storage, or while transfer the image and sometimes while handling the devices. Therefore the need to enhance the image is crucial in order to improve the image quality. Segmentation technique for Magnetic Resonance Imaging (MRI) of the brain is one of the method used by radiographer to detect any abnormality happened specifically for brain. In this study we have proposed a method for segment the normal and abnormal tissues in the MRI images. At first we select the input image from the BMRI database. Then apply the skull stripping method to the input brain image. After that the proposed method perform the segmentation technique with the help of improved artificial neural networks here the weights are optimized by means of adaptive genetic algorithm. After the classification, the normal tissues like White Matter (WM), Grey Matter (GM) and Cerebrospinal Fluid (CSF) are segmented from the normal BMRI image. Abnormal tissues like Tumor and Edema are segmented from the abnormal BMRI images. The abnormal tissue segmentation will be carried out by optimal SVD and HHT based segmentation in which optimization can be done by Binary cuckoo search algorithm. Both the classification and the segmentation performance of the proposed technique are evaluated in terms of accuracy, sensitivity and specificity. The implementation of the proposed method is done in the working platform of MATLAB.

Hybrid SVD Based Hilbert Huang Transform (HSHHT) Technique for Abnormality Detection in Brain MRI Images