Shannon and Non Shannon Entropy Based MRI Image Segmentation

Image Segmentation plays an important role in medical field that enable professionals to detect their patient’s problems and help them to get proper diagnosed. In this article, an entropy based approach for image segmentation is discussed to highlight tumor in MRI images. Magnetic Resonance Imaging (MRI) is an imaging, in which pixel values are based on radiation absorption. In the proposed approach threshold values are selected on the basis of different entropy measures such as Shannon, Renyi, Havrda Charvat, Kapur and Vajda entropy measures to segmentise an MRI image indicating tumor. The gray level co-occurrence and probability matrix are utilized as basis functions for proposed methodology. Simulation results for different entropy measures depicts that Non Shannon Entropy measures give more promising results as compared to classical Shannon based approach, thus can be used to detect human body tumors using MRI images.