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


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.


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.


Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.