Feature extraction of the brain tumours with the help of MRI, based on symmetry and partitioning

Computer-aided diagnostic (CAD) studies are used for scientific observations for explanation since very long time, but they are extraordinarily powerful to perform completely machine-driven algorithmic analyses for brain magnetic resonance imaging lesions. Structural and purposeful imbalance within the human brain could be reviewed. This imbalance analysis of the brain has terrific importance in an image analysis. In the present work, the imbalance between the two hemispheres is considered as the base for the detection of the tumour. We have segmented the brain into the two halves using thresholding technique, followed by statistical feature extraction for the double authentication of the existence of tumour which proves to be the better approach. The approach also takes into consideration corrections needed for the tilt observed while capturing the MRI

TEXTURE ANALYSIS OF BRAIN TUMOR IN DIGITIZED MRI USING GLEASON AND MENHINICK DIVERSITY INDEX

Tumor is swelling of the body part, due to this abnormal growth of cells in that place of the body. If it is in brain called brain tumor. Brain tumor is diagnosed by the magnetic resonance imaging (MRI). In the propose methodology, we firstly detect and extract tumor using watershed segmentation. To increase the efficiency of texture feature extraction, the diversity index’s capability to detect patterns of tumor. The Gleason and Menhinick indexes are used. At the end, the extracted texture of brain tumor image is classified using the Support Vector Machine, looking to differentiate the malignant and benign class of tumor