OPTIMIZED FUZZY LOGIC APPLICATION FOR MRI BRAIN IMAGES SEGMENTATION

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 cm 2 .

[1]  Tzong-Jer Chen,et al.  Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..

[2]  Christos Davatzikos,et al.  Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme , 2009, Magnetic resonance in medicine.

[3]  Jerry L. Prince,et al.  Adaptive fuzzy segmentation of magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[4]  D. Pham.  Unsupervised tissue classification in medical images using edge-adaptive clustering , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[5]  Supun Samarasekera,et al.  Multiple sclerosis lesion quantification using fuzzy-connectedness principles , 1997, IEEE Transactions on Medical Imaging.

[6]  James C. Bezdek,et al.  A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain , 1992, IEEE Trans. Neural Networks.

[7]  Somayeh Alizadeh,et al.  Using Data Mining for Learning and Clustering FCM , 2008 .

[8]  Benoit M. Dawant,et al.  Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study , 1993, IEEE Trans. Medical Imaging.

[9]  J. Bisese.  Cranial Mri: A Teaching File Approach , 1991 .

[10]  Jerry L. Prince,et al.  Finding the Brain Cortex Using Fuzzy Segmentation, Isosurfaces, and Deformable Surface Models , 1997, IPMI.

[11]  William E. Higgins,et al.  Relaxation methods for supervised image segmentation , 1997 .

[12]  Hong Yan,et al.  Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images , 2006 .

[13]  Aly A. Farag,et al.  Two-stage neural network for volume segmentation of medical images , 1997, Pattern Recognit. Lett..

[14]  Jagath C. Rajapakse,et al.  Statistical approach to segmentation of single-channel cerebral MR images , 1997, IEEE Transactions on Medical Imaging.

[15]  Wenhui Yang,et al.  A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images , 2003, DICTA.