Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

作     者：A.Renugambal K.Selva Bhuvaneswari 

作者机构：Department of MathematicsUniversity College of Engineering KancheepuramKanchipuram631552India Department of Computer Science and EngineeringUniversity College of Engineering KancheepuramKanchipuram631552India 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2020年第64卷第8期

页      面：681-700页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The author(s) received no specific funding for this study 

主　　题：Hybrid WCMFO algorithm Otsu’s function multilevel thresholding image segmentation brain MR image 

摘      要：In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image *** constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization *** optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the *** test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image *** experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence *** contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time ***,it was observed thatthe segmented image gives greater detail when the threshold level ***,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed ***,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.