@ARTICLE{ iet:/content/journals/10.1049/iet-cvi.2018.5265, author = {Lakshmipriya Balagourouchetty}, author = {Jayanthi K. Pragatheeswaran}, author = {Biju Pottakkat}, author = {Ramkumar Govindarajalou}, keywords = {liver lesion diagnosis;liver-related pathologies;computed tomography acquisition;unenhanced CT images;enhanced images;kidney disorders;enhanced output;contrast enhanced computed tomography images;hepatocellular carcinoma;invasive technique;novel enhancement technique;lesion characterisation;diabetes;CT acquisition;image enhancement approach;primary liver cancer;intravenous contrast agent;CECT imaging technology;liver lesions;}, ISSN = {1751-9632}, language = {English}, abstract = {Hepatocellular carcinoma, the primary liver cancer and other liver-related pathologies are diagnosed with the help of contrast enhanced computed tomography (CECT) images. The CECT imaging technology is claimed to be an invasive technique, as the intravenous contrast agent injected prior to computed tomography (CT) acquisition is harmful and is not advised for patients with pre-existing diabetes and kidney disorders. This study presents a novel enhancement technique for the diagnosis of liver lesions from unenhanced CT images by means of fuzzy histogram equalisation in the non-sub-sampled contourlet transform domain followed by decorrelation stretching. The enhanced images obtained in this study substantiate that the proposed method improves the diagnostic value from the unenhanced CT images thereby providing an alternate painless solution for CT acquisition for the subset of patients mentioned above. Another major highlight of this work is the characterisation of lesions from the enhanced output for five different classes of pathology. The obtained results presented in this study demonstrate the potency of the proposed enhancement technique in achieving an appreciable performance in lesion characterisation. The images used for this research study have been obtained from Jawaharlal Institute of Medical Education and Research Puducherry, India.}, title = {Enhancement approach for liver lesion diagnosis using unenhanced CT images}, journal = {IET Computer Vision}, issue = {8}, volume = {12}, year = {2018}, month = {December}, pages = {1078-1087(9)}, publisher ={Institution of Engineering and Technology}, copyright = {© The Institution of Engineering and Technology}, url = {https://digital-library.theiet.org/;jsessionid=1od5vtd0xeqk6.x-iet-live-01content/journals/10.1049/iet-cvi.2018.5265} }