2D Medical Image Segmentation

This paper studies and proceeds with different types of medical data. Medical image analysis always deals with challenges of good and diagnosing of different medical abnormalities visualized with Rontgen, MRI, or CT data. Imagination techniques in medicine play important roles in the diagnostic of pathological objects and diseases in the human body. Clinical doctors make it in real time but very often they ask for additional consultations of the image results. We created two of our own software in C++ and MATLAB for implementing the GraphCut method and a GrabCut method. We improved these two algorithms and optimized them from a time point of view and from giving better segmentation of some medical objects with improved and sharper boundary. We improved segmentation of 2D data image for medical image analyses and in this way we have contributed to better medical diagnoses of different pathological objects in the human body. The aim is to help clinical doctors with more precise diagnostic of some specific objects in the body. The real online diagnostic process is not always sufficient for clinical doctors.