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- W2070610592 abstract "ABSTRACT This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information. Firstly, we classify the image into the region of interest and background using fuzzy c means algorithm. Then we use the information of the tissues gradient and the intensity inhomogeneities of regions to improve the quality of segmentation. The sum of the mean variance in the regi on and the reciprocal of the mean gradient along the edge of the region are chosen as an objective function. The minimum of the sum is optimum result. The result shows that the clustering segmentation algorithm is effective. Keywords: MRI, brain tumor, FCM, gradient, inhomogeneity 1. INTRODUCTION There are three pure brain tissue classe s in brain MR images, gray matter, white matter, and cerebrospinal fluid, 1 Brain tumor segmentation is a process to separate the different tumor tissues from those normal ones. MRI, as a powerful and mature technology, can provide various information about brain tissue, and is becoming extremely important in clinical diagnosis." @default.
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- W2070610592 date "2009-10-30" @default.
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- W2070610592 title "MRI brain tumor segmentation based on improved fuzzy c-means method" @default.
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- W2070610592 doi "https://doi.org/10.1117/12.832577" @default.
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