A Novel Framework for Fully Automated ROI Segmentation of Brain MR Images
Keywords:
MRI, SVM, Rician noise, Contrast Enhancement, Classification.Abstract
Brain region-of-interest (ROI) segmentation based on structural magnetic resonance imaging (MRI) scans is an essential step for many computer-aid medical image analysis applications. Due to low intensity contrast around ROI boundary and large inter-subject variance, it has been remaining a challenging task to effectively segment brain ROIs from structural MR images. The proposed system is the implementation of noise removal and segmentation algorithm. The Rician noise in MRI (Magnetic Resonance Image) degrades the image quality and thus, accuracy in segmentation is reduced and localization of brain may not be precise. The proposed system is a robust approach is proposed which estimates and removes the Rician noise of MRI for improving segmentation and detection of tumours. First, a robust Rician noise estimation algorithm is employed to identify all the pixels with high Rician noise. Second, a bilateral filter based denoising algorithm is employed to filter image in the wavelet domain. Successively a bilateral filter parameter optimization method is adopted, which uses the noise, contrast and frequency components in MRI to select suitable filter parameters for Bilateral Filter (BF). It is suitable for edge preserving and for adaptive denoising to segment image correctly. Further, after denoising the image, the contrast of the image is improved as a pre-processing step before the image segmentation. Next, SVM-based image segmentation algorithm is employed to segment the MRI .
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