Bayesian classification using DCT features for brain tumor detection
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
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Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particulary during the clinical analysis of magnetic resonance (MR) brain image. Fuzzy c-means (FCM) clustering algorithm has been widely used in many medical image segmentations. However, the conventionally standard FCM algorithm is sensitive to noise because of not taking into account the spatial information. To overcome the above problem, a modified FCM algorithm (called mFCM later) for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster. The proposed algorithm is applied to both artificial synthesized image and real image. Segmentation results not only on synthesized image but also MRI brain image which degraded by Gaussian noise and Salt-Pepper noise demonstrates that the presented algorithm performs more robust to noise than the standard FCM algorithm.