AIRCC's International Journal of Computer Science and Information Technology

Indah Soesanti ¹, Adhi Susanto ¹, Thomas Sri Widodo ¹, Maesadji Tjokronagoro ²

Affiliations
1 Department of Electrical Engineering and Information Technology, Gadjah Mada University, Yogyakarta, ID
2 Gadjah Mada University, Yogyakarta, ID

Abstract

In this paper, an optimized fuzzy logic method for Magnetic Resonance Imaging (MRI) brain images segmentation is presented. The method is a technique based on a modified fuzzy c-means (FCM) clustering algorithm. 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 advantages of the algorithm are that it is less sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. Originality of this research is the methods applied on a normal MRI brain image and MRI brain images with tumor, and analyze the area of tumor from segmented images. The results show that the method effectively segmented MRI brain images with spatial information, and the segmented MRI normal brain image and MRI brain images with tumor can be analyzed for diagnosis purpose. In order to identify the area of abnormal mass of MRI brain images with tumor, it is resulted that the area is identified from 8.38 to 25.57 cm².

Keywords

Adaptive Image Segmentation, Optimized Fuzzy Logic, MRI Brain Image, Fuzzy Membership Function.

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