Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

作     者：J.Jayanthi M.Kavitha T.Jayasankar A.Sagai Francis Britto N.B.Prakash Mohamed Yacin Sikkandar C.Bharathiraja 

作者机构：Department of Computer Science and EngineeringSona College of TechnologySalem636005TamilnaduIndia Department of Electronics and Communication EngineeringK.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication EngineeringUniversity College of EngineeringAnna UniversityTiruchirappalli620024India Department of Mechanical EngineeringRohini College of Engineering and TechnologyPalkulam629401India Department of Electrical and Electronics EngineeringNational Engineering CollegeKovilpatti628503India Department of Medical Equipment TechnologyCollege of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Kingdom of Saudi Arabia Department of Electrical and Electronics EngineeringSRM Institute of Science and TechnologyChennai603203India 

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

年 卷 期：2021年第68卷第9期

页      面：4191-4203页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Brain tumor TLBO algorithm skull stripping preprocessing segmentation 

摘      要：Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this *** various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal *** only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the *** diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of *** the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)*** of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant *** of image processing techniques are available for processing the images to identify the affected *** study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI *** involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization *** traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting *** results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its *** experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.