Brain Tumor Statistical Structure Analysis And Prediction Based On Self Organised Map
Magnetic resonance (MR) imaging has several advantages over other medical imaging modalities, including high contrast among different soft tissues, relatively high spatial resolution across the entire field of view and multi-spectral characteristics. Therefore, it has been widely used in quantitative brain imaging studies. This Paper analyses various clustering techniques to track tumor objects in Magnetic Resonance (MR) brain images. The input to this system is the MR image of the axial view of the human brain. Automated MRI (Magnetic Resonance Imaging) brain tumor segmentation is a difficult task due to the variance and complexity of tumors. In this paper, a statistical structure analysis based tumor segmentation scheme is presented, which focuses on the structural analysis on both tumorous and normal tissues. Firstly, two kinds of features including intensity-based, symmetry-based and texturebased are extracted from structural elements. Then a classification technique using AdaBoost that learns by selecting the most discriminative features is proposed to classify the structural elements into normal tissues and abnormal tissues.
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