An anisotropically diffusible fuzzy clustering based image segmentation methodology for intracranial cyst detection

Abstract

An Intracranial cyst is an abnormal growth of mass in the brain that affects functioning of the nervous system and so an early detection of the lesion enables to avoid adverse effects. The processing unit in the Magnetic Resonance Imaging (MRI) system performs reading the images followed by primary image enhancement to suppress distortions thereby enhancing the feature quality in terms of its intensity, augmenting the resolution by image segmentation, post-processing by thresholding based on grayscale values and performing several morphological operations. With the existing methodologies, extracting the Region Of Interest (ROI) with the overlapping intensity values lead to inaccurate results. A novel method in which the input image that is anisotropically diffused and blurred is converted into a sharp image. Further, fuzzy partitioning of pixels deployed on Global Thresholding –Clustering Methodology (GT-CM) based segmentation takes 4 clusters into account hence forth seperating the exterior portion of the skull, the border region of the skull, the ventricles which may include the lesion and the noise. Statistical results based on several metrics such as sensitivity, specificity, F measure, Jaccord Index, Dice Coefficient and precision show that the proposed method is far more effective. An accuracy of 99.26% is obtained in exactly locating and extracting the lesion along with its attributes.