A methodology for analysis extraction and visualization of CT scans

Compared to MRI computer tomography (CT) images have a very narrow signal attenuation range for soft tissues and very strong one for bones and air. The goal of this study is to design a simple reliable model to read, quantify, extract, and visualize the anatomical region of the liver from a colorectal CT scan. The work introduced is considered an important initial step for the development of computer assisted diagnosis (CAD) systems or for 3D reconstruction in realistic voxel-based rendering models. The methodology presented follows a two-stage approach. Initially, the angio-abdominal CT image is carefully analyzed to amplify the CT soft tissues´ signals. The task is achieved by optimizing the threshold values for 2-D visualization, background discrimination, and identification of the CT slice with the largest liver bulk. Consequently, a technique is proposed to granulate the images on a per slice basis. The intensity based granulation technique is set to 9.9 urn similarity difference and supported by strongly connected image map created from extracted features with 98% neighborhood ratio´ threshold. The proposed two-step methodology was successfully tested on 181 colorectal CT scans.