Optimized Fuzzy Logic Based Segmentation for Abnormal MRI Brain Images Analysis

• Published in: International journal of computer science issues Vol. 8; no. 5; p. 207
• Main Authors: Soesanti, Indah, Susanto, Adhi, Widodo, Thomas Sri, Tjokronagoro, Maesadji
• Format: Journal Article
• Language: English
• Published: Mahebourg International Journal of Computer Science Issues (IJCSI) 01.09.2011
• Subjects: Algorithms; Brain; Computer science; Fuzzy logic; Image analysis; Mathematical analysis; Mathematical models; Segmentation
• ISSN: 1694-0814; 1694-0784

In this paper an optimized fuzzy logic based segmentation for abnormal MRI brain images analysis is presented. A conventional fuzzy c-means (FCM) technique does not use the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The FCM algorithm that incorporates spatial information into the membership function is used for clustering, while a conventional FCM algorithm does not fully utilize the spatial information in the image.The advantage of the technique is less sensitive to noise than the others. Originality of this research is focused in application of the technique on a normal and a glioma MRI brain images, and analysis of the area of abnormal mass from segmented images. The results show that the method effectively segmented MRI brain images, and the segmented normal and glioma MRI brain images can be analyzed for diagnosis purpose. The area of abnormal mass is identified from 7.15 to 19.41 cm super( 2).