#6241. A novel triple-level combinational framework for brain anomaly segmentation to augment clinical diagnosis

September 2026publication date
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Journal’s subject area:
Medicine
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Abstract:
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.
Keywords:
Automatic anomaly detection; FCM; GLCM; Jaya Algorithm; metaheuristic optimisation

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