This paper is published in Volume-3, Issue-1, 2017
Area
Image Processing
Author
Kalyani R. Mandlik
Co-authors
Dr. Suresh S. Salankar
Org/Univ
G.H. Raisoni College of Engineering, Nagpur, Maharashtra, India
Pub. Date
09 February, 2017
Paper ID
V3I1-1325
Publisher
Keywords
Magnetic Resonance Image (MRI), Brain Tumor Segmentation, Bias-Corrected Fuzzy C Means Algorithm (BCFCM).

Citationsacebook

IEEE
Kalyani R. Mandlik, Dr. Suresh S. Salankar. Performance Analysis of Image Clustering Algorithm Applied to Brain MRI, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Kalyani R. Mandlik, Dr. Suresh S. Salankar (2017). Performance Analysis of Image Clustering Algorithm Applied to Brain MRI. International Journal of Advance Research, Ideas and Innovations in Technology, 3(1) www.IJARIIT.com.

MLA
Kalyani R. Mandlik, Dr. Suresh S. Salankar. "Performance Analysis of Image Clustering Algorithm Applied to Brain MRI." International Journal of Advance Research, Ideas and Innovations in Technology 3.1 (2017). www.IJARIIT.com.

Abstract

Exact measurements in brain diagnosis are difficult because of various shapes, area, and appearances of tumors. The tumor is an abnormal growth of body tissue; it can be cancerous or non-cancerous. There is a strong demand for automating the tumor detection and segmentation process. Thus, we required the computer aided diagnosis of brain tumor from MRI images to control the difficult problems in the manual segmentation. There are several methods available in the literature for medical image segmentation. In this paper, we introduced new segmentation method for detection of bias field image and classification of white matter and gray matter.From experimental results, BCFCM required less time than FCM algorithm.
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