Abstract
Image segmentation plays crucial role in medical analysis and forms the basis tor clinical diagnosis and patient's treatment planning. But the large variation in organ shapes, inhomogeneous intensities, poor contrast, organic nature of textures and complex boundaries in medical images makes segmentation process adverse and challenging. Further, the absence of annotated ground-truth dataset in medical field limits the advantages of the trending deep learning techniques causing several setbacks. Though numerous unsupervised methods are reported in literature to combat the challenges in medical domain, achieving better segmentation quality still remains as an open issue. This mirk aim to address this issue integrating the strength of multiresolution analysis and the meta heuristic optimization techniques for unsupervised medical image segmentation. The proposed approach employs undecimated wavelet frames to extract translation invariant texture and gray information at different orientations to effectively characterize the textures M medical images. Next, the approach introduces the latest meta-heuristic whale optimization algorithm (\VOA), the global optimizer to enhance the performance c unsupervised clustering algorithm With optimized cluster centers to cluster the extracted Wavelet texture features. Moreover; the study contributes to fill the gap in hterature investigating for the first time different intelligence algorithms such as fuzzy, genetic algorithm (GA) and particle swarm optimization (PSO) for unsupervised medical image segmentation to demonstrate the efficacy of the proposed approach. Evaluation was performed on medical CT and MR images based on 'feature similarity (FSIM), dice (DC) and 'feature of merits (F 0Ms). Experimental results demonstrates the supremacy of the proposed approach over other intelligence algorithms. Finally, statistical study with ANOVA analysis was carried out to confirm the significance of the proposed approach in determining the optimal solution and displaying promising segmentation results toward diagnostic support for radiologist. (C) 2020 The Authors. Published by Atlantis Press SARL.