A comparative review of optimisation techniques in segmentation of brain MR images

Issue title: Special section: Intelligent data analysis and applications & smart vehicular technology, communications and applications

Guest editors: Valentina Emilia Balas and Lakhmi C. Jain

Article type: Research Article

Authors: Naga Srinivasu, P.^{a; *} | Srinivasa Rao, T.^a | Dicu, Anca M.^b | Mnerie, Corina Anca^b | Olariu, Iustin^c

Affiliations: [a] Department of CSE, GIT, GITAM Deemed to be University, Visakhapatnam, AP, India | [b] Aurel Vlaicu University of Arad, Arad, Romania | [c] Vasile Goldis Western University of Arad, Arad, Romania

Correspondence: [*] Corresponding author. P. Naga Srinivasu, Tel.: +91 900 0595 535; E-mail: parvathanenins@gmail.com.

Abstract: MRI image segmentation, a challenging task in medical diagnostics aids in extracting the summarized information of the anatomy of the human brain, thereby offering the potentiality for accurate treatment of the disease. The purpose of this work is to elevate the performances of different optimization techniques that are used in automated segmentation procedures. The performance of four algorithms was evaluated quantitatively over the genetic algorithm-based segmentation, which is the prevailing approach in automated segmentation. The upshot exhibits the accuracy and performance of various optimization techniques with a genetic algorithm.

Keywords: Genetic algorithm, harmonic mean, magnetic resonance imaging, SVM, PSO, TLBO

DOI: 10.3233/JIFS-179688

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6031-6043, 2020