Liver Segmentation of 3D CT Scan images using Parallel Processing

This study describes a new 3-D liver segmentation method for purpose of transplantation surgery as a treatment for liver tumors. Liver segmentation is not only the key process for volume computation but also fundamental for further processing to get more anatomy information for individual patient. Due to the low contrast, blurred edges, large variability in shape and complex context with clutter features surrounding the liver that characterize the liver CT images, it is a convoluted problem and still a challenge task to robustly and accurately segment the liver. In this paper, we overcome these difficulties with a novel variational model based on the idea of intensity probability distribution propagation and region appearance propagation with which we can focus on the target liver regardless of how complex the uninterested background is. This 3-D segmentation is based on combining a modified k-means segmentation method with a special localized contouring algorithm. De noising of the image is done in order to obtain good results for the technique .Histograms are plotted so as have a graphical view of the difference between noisy and de noised image. In the segmentation process in order to divide the image, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction