• 1 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia,
Interdiscip Sci, 2014 Sep;6(3):222-34.
PMID: 25205500 DOI: 10.1007/s12539-013-0204-7


Hepatocellular Carcinoma is the most common type of liver cancer having a strong relation with cirrhosis. Undoubtedly, cirrhosis may be caused by the virus infection of hepatitis B (HBV) and hepatitis C (HBC) or through alchoholism. However, even when cirrhosis has not been developed, patients with hepatitis viral infections are still at the risk of liver cancer. Apparently, among the numerous medical imaging techniques, Computed Tomography (CT) is the best in defining liver tumor borders. Unfortunately, these imaging techniques, including the CT procedures, usually rely on an appended application to reconstruct the generated 2-D slices to 3-D model. This may involve high performance computation, may be time-consuming or costly. Moreover, even with the outstanding performances of CT in defining the liver tumor boundaries, contrast between tumor tissues and the surrounding liver parenchyma is too low in CT slices. With such a close proxity in the tumor and the surrounding liver tissues, accurate characterization of liver tumor is a challenge. Previously, algorithms were developed to reveal abnormalities in brain's MRI datasets and CT abdominal pelvic, however, introducing a framework that could accurately characterize liver tumor and its surrounding tissues in CT datasets would go a long way in contributing to medical diagnosis and therapy planning of Hepatocellular Carcinoma. This paper proposes an Hepatocellular Carcinoma framework by extending the functionalities of SurLens Visualization System with an automatic liver tumor localization technique using Compute Unified Device Architecture (CUDA). The study was evaluated with liver CT datasets from the Imaging Science and Information Systems (ISIS) Center, the Georgetown University Medical Center. Significantly, visualization of liver CT datasets and the localization of the entangled tumor was achieved without prior datasets segmentation. Interestingly, the framework achieved remarkably good processing speed at a reasonably cheaper cost with an immediate reconstruction of the datasets and mapping of the tumor tissues within the surrounding liver parenchyma.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.