Segmentation of tissues in MR images using Modified Spatial Fuzzy C Means algorithm

Abstract

MRI brain images are widely used in medical applications for research, diagnosis, treatment, surgical planning and image guided surgeries. These MR brain images are often corrupted with Intensity Inhomogeneity artifact 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 and 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 method is proposed which automatically segments normal tissues such as White Matter, Gray Matter and Cerebrospinal Fluid from MR images with Rician noise and Intensity Inhomogeneity artifact. The proposed method consists of preprocessing using wrapping based curvelet transform to remove noise and Modified Spatial Fuzzy C Means segments normal tissues by considering spatial information because neighboring pixels are highly correlated and also construct initial membership matrix using initial cluster center by incorporating spatial neighborhood information to improve strength of clustering. This combination reduces Intensity Inhomogeneity artifact by providing higher segmentation accuracy. In this proposed work, the accuracy, sensitivity and specificity are improved with better segmentation over other previous methods.