A smartly designed automated map based clustering algorithm for the enhanced diagnosis of pathologies in brain MR images

The competitive segmentation of fuzzy clustering is utilized in a greater manner to deal with the local spatial information of input medical images. Fuzzy clustering favours lesions and tumour identification through the segmentation process where less accuracy attainment and time complexity might be instigated for the identification of oddities. To rectify the above-said problems, a novel methodology that encapsulates the combination of unsupervised neural network and fuzzy clustering processes, which effortlessly distinguishes the lesion and tumour region in MR brain images is developed through this study. The initial process of the proposed algorithm employs the histogram-based feature extraction of the input images; whereof, a feature vector selection is made for the operation of self-organizing map (SOM), which is a neural network functionary that progresses through the mapping process. Modification regarding the membership function of fuzzy entropy clustering (MFEC) is done based on the entropy value of the input image that results in quicker convergence. Finally, the updated objective function of MFEC algorithm augments the SOM result. It is found that the proposed SOM based MFEC algorithm is superior to other traditional segmentation algorithms, which have rendered the better visible understanding of the image. Further, the end-results of the algorithm are verified through the evaluation of quantity metrics using ground truth of the brain MR images. The proposed SOM based MFEC algorithm precisely provides 82.26% of Jaccard value and 90.05% of Dice Overlap Index value, and these values prove better brain slices segmentation and provide enormous help to radiologists during patient diagnosis.