An optimization based framework for region wise optimal clusters in MR images using hybrid objective

摘要

Swarm Intelligence based methods are amongst the highly efficient approaches for optimization in image clustering. Optimal clustering has been studied in many real-world applications, such as medical and aer-ial image segmentation. Region wise clustering is a class of challenges in image region segmentation. Uncertain convergence and high computational load are critical issues in the region-wise image cluster-ing due to local optimum and the NP-hard cluster computation. Meta-heuristics approaches are efficient to achieve global optimum by including better search space exploration techniques. This paper develops a framework for cluster optimization by selecting the seeds in pathological medical resonance (MR) images using a variant of firefly optimization. The heuristics based method uses Gaussian random walk for convergence that occasionally results in local optima; therefore, we have investigated the firefly method with more search space exploration techniques and improved region-wise objective. Our frame-work applies the levy flights for exploration and compared with other search spaces like Cauchy, and Gaussian random walk. The intra-cluster and inter-cluster-based hybrid objective is converged swiftly. The framework has been compared with two of its variants and three other meta-heuristic-based meth-ods, namely simulated annealing, PSO, and Cuckoo search. The MSE, PSNR, structural similarity(SSIM), and feature similarity(FSIM) based evaluation indices are measured for normal and abnormal MR images and listed in table-5, 6. Reported indices values for our frame work are better than existing methods. Figure-9, 10, 11 compares the stochastic search spaces among Levy flights, Cauchy random walk, Gaussian random walk and observed Levy flights as better search space. In section-3.5, The convergence for proposed framework is shown for multi-objective function against the single objective in two normal and abnormal images. Single objective converged from 188 to 119 and multi-objective converged from 196 to 117 for first image. For second image, the single objective converged from 168 to 94 and multi -objective converged from 183 to 93. Finally, we have illustrated the convergence criteria and computa-tion complexity on publicly available MR data sets.(c) 2023 Elsevier B.V. All rights reserved.