Comparison on Automated Brain Tumor Detection and Segmentation Approaches for MRI Brain Images

P. G. K. Sirisha*, D. Haritha**
*-** Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.
Periodicity:July - September'2019
DOI : https://doi.org/10.26634/jip.6.3.16322

Abstract

Magnetic Resonance Imaging (MRI) technology is used to study the internal structure of brain in the form of digital images. The accurate detection of tumor region in the brain images is a challenging task. Brain tumor detection is an important task for doctors to give better treatment for the patients. Brain tumor regions can be effectively identified and located by segmentation of MRI brain images. This paper discuss and compares the efficiency of two novel optimization methods for Detection and Segmentation of MRI brain images namely “Shuffled Frog Leaping Algorithm (SFLA)- Expected Maximization (EM) frame work” and “Shuffled Frog Leaping Algorithm (SFLA) –Tabu Search (TS) frame work” for brain tumor detection in 2D MRI brain images. The results obtained had been compared with Particle Swarm Optimization Incorporating Fuzzy C Means Clusrering (PSO-FCM) method and EM methods. Finally, the results show that SFLA-TS method gives better results when compared to SFLA-EM method in identifying tumor regions in 2D MRI brain images.

Keywords

Brain Tumor detection, Segmentation, SFLA,EM,TS

How to Cite this Article?

Sirisha, P. G. K., and Haritha, D. (2019). Comparison on Automated Brain Tumor Detection and Segmentation Approaches for MRI Brain Images. i-manager's Journal on Image Processing, 6(3), 24-32. https://doi.org/10.26634/jip.6.3.16322

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