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Medical image segmentation techniques have become a very imperative prerequisite for accuracy and easiness of diagnosis in image analysis processes. An effective and novel segmentation technique is still needed for the hour, as it is very challenging for medical practitioners to differentiate the normal and pathological brain tissues in unaided sight. To combat the above limitation, the researchers proposed a robust algorithm for the segmentation of MR brain image based on a novel combination of FCM for segmentation, GLCM for feature extraction and Jaya Algorithm (JA) for optimisation. The combination of GLCM and FCM improves the accuracy of the operation by incorporating the neighbourhood spatial characteristics. Then, the study uses the JA to find the optimal threshold value. At last, the segmented images are subjected to threshold operation using the optimal value. The ground truth images validate the effectiveness of the proposed algorithm, and it also uses the domain-specific parameters such as Peak Signal to Noise Ratio (PSNR) index, Dice Coefficient (DCI) index, Mean Squared Error (MSE) index, Jaccard-Tanimoto Coefficient (JTC) index and Hausdorff Distance (HD). The proposed algorithm delivers JTC and DCI values of 69.3% and 81.9%, respectively, the highest among the other traditional algorithms specified in this work.

A novel triple-level combinational framework for brain anomaly segmentation to augment clinical diagnosis