Abstract
In this paper, a method for 3D medical image segmentation is presented. This method is used to detect brain tumor in MRI images by combining Clustering and Classification methods to decrease the complexity of time and memory. In the first phase, non-negative matrix factorization with sparseness constraint method is used to separate the region of interest from the image. In the second phase, the classification of the region of interest is performed. For this purpose, TOP-LBP features and gray level co-occurrence matrix are extracted and Random forest is used for classification and segmentation of the necrosis, edema, non-enhanced tumor and enhanced tumor. This method has achieved a fast speed for segmentation of MRI 3D images and has been evaluated with criteria of Dice's and Jacquard's coefficient on the brain tumor from magnetic resonance image obtained from the Brats2013 database.
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