Brain MRI Segmentation using Texture Features
Use of GLCM texture features
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The main aim of this book is to introduce to a system which can detect brain tumor using brain Magnetic Resonance Image segmentation. Automated MRI (Magnetic Resonance Imaging) brain tumor segmentation is a difficult task due to the variance and complexity of tumors. In this work, a statistical structure analysis based brain tissue segmentation scheme is presented, which focuses on the structural analysis on both abnormal and normal tissues. As the local textures in the images can reveal the typical regularities of biological structures, textural features have been extracted using co-occurrenc...
The main aim of this book is to introduce to a system which can detect brain tumor using brain Magnetic Resonance Image segmentation. Automated MRI (Magnetic Resonance Imaging) brain tumor segmentation is a difficult task due to the variance and complexity of tumors. In this work, a statistical structure analysis based brain tissue segmentation scheme is presented, which focuses on the structural analysis on both abnormal and normal tissues. As the local textures in the images can reveal the typical regularities of biological structures, textural features have been extracted using co-occurrence matrix approach. By the analysis of level of correlation the number of features can be reduced to the significant components. Feed forward back propagation neural network is used for classification. Proposed techniques of analysis and classification are used to investigate the differences of texture features among macroscopic lesion white matter (LWM) and normal appearing white matter (NAWM) in magnetic resonance images (MRI) from patients with normal and abnormal white matter.
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