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Automatic Tissues Segmentation of Skull Stripped MR Brain Images using Modified Spatial Possibilistic Fuzzy C Means Algorithm

D. Selvathi, R. Dhivya

Abstract


MRI brain images are widely used in medical applications for research, diagnosis, treatment and image guided surgeries. These MR brain images are often corrupted with Intensity Inhomogeneity artifact, that cause unwanted intensity variation due to non- uniformity in RF coils and Rician noise, the dominant noise in MRI due to thermal vibrations of electrons, ions and movement of objects during acquisition which may affect the performance of image processing techniques used for brain image analysis. Due to this type of artifact and noises, sometimes one type of normal tissue in MRI may be misclassified as other type of normal tissue and it leads to error during diagnosis. In this work, a Modified Spatial Possibilistic Fuzzy C Means method is proposed to automatically segments normal tissues such as White Matter, Gray Matter and Cerebrospinal Fluid from MR brain images with Rician noise and Intensity Inhomogeneity artifact. Accurate segmentation of Normal Tissues used to quantify volume changes in these tissues for clinical diagnosis to identify the diseases due to delineation of tissues by the medical practitioner. The proposed method consists of two preprocessing steps, (1) wrapping based curvelet transform to remove noise, (2) Skull stripping using Mathematical Morphology and then the proposed method used for segmentation of normal tissues. In the Existing work, Modified Spatial Fuzzy C Means was used for segmenting normal tissues. In which, spatial function is incorporated with Standard FCM and also to avoid initialization problem SFCM is modified by generating initial membership matrix using spatial information. This gives better accuracy for low level combination of noise and artifact. But for high level of combination, the accuracy difference was high. To overcome effect of noise, the Possibilistic function is incorporated with membership function in MSFCM. This combination reduces Intensity Inhomogeneity artifact by providing higher segmentation accuracy than existing MSFCM method with skull stripping. In this proposed work, the accuracy, sensitivity and specificity are improved with better segmentation over other previous methods.

Keywords


Wrapping based Curvelet, Mathematical Morphology, Possibilistic Fuzzy C Means, Segmentation, Magnetic Resonance Imaging.

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