Machine vision-based Statistical texture analysis techniques for characterization of liver tissues using CT images Authors Mehrun Nisa Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan Saeed Ahmad Buzdar Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan Muhammad Arshad Javid Department of Basic Sciences (Physics), University of Engineering and Technology, Taxila, Pakistan Muhamamd Saeed Ahmad Department of Computer Sciences and Information Technology, Government Sadiq College Women University, Bahawalpur, Pakistan Ayesha Ikhlaq Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan Sadia Riaz Department of Diagnostic Radiology, Bahawal Victoria Hospital, Bahawalpur, Pakistan DOI: https://doi.org/10.47391/JPMA.4138 Abstract Objective: To characterize human liver tissues by demonstrating the ability of machine vision, and to propose a new auto-generated report based on texture analysis that may work with co-occurrence matrix statistics. Method: The retrospective study was conducted at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan, and comprised clinically verified computed tomography imaging data between October 2018 and September 2020. The image samples and related data were used to segregate classes 1-4. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues using supervised learning methods, principal component analysis, linear discriminant analysis, and non-linear discriminant analysis. Robust and reliable texture features were investigated by generating testing classes. Overall performance of the presented machine vision approach was analyzed using four parameters; precision, recall/sensitivity, F1-score, and accuracy. Statistical analysis was done using B11 software. Results: There were 312 image samples from 71 patients; 51(71.8%) males and 20(28.2%) females. Among the patients, 19(26.7%) had abscess, 15(21.1%) had metastatic disease, 23(32.4%) had tumour necrosis, 6(8.5%) had vascular disorder, and 8(11.3%) were normal. Principal component analysis, linear discriminant analysis, and non-linear discriminant analysis showed high >97.86% values, but the discrimination rate was 100% for class 4. Conclusion: Abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques using second-order statistics that may assist the radiologist and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases. Key Words: Liver abscess, Computed tomography imaging, Liver diseases, Image processing. Downloads Full Text Article Published 2022-08-15 How to Cite Mehrun Nisa, Saeed Ahmad Buzdar, Muhammad Arshad Javid, Muhamamd Saeed Ahmad, Ayesha Ikhlaq, & Sadia Riaz. (2022). Machine vision-based Statistical texture analysis techniques for characterization of liver tissues using CT images. Journal of the Pakistan Medical Association, 72(9), 1760–1765. https://doi.org/10.47391/JPMA.4138 More Citation Formats ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Download Citation Endnote/Zotero/Mendeley (RIS) BibTeX Issue Vol. 72 No. 9 (2022): SEPTEMBER Section RESEARCH ARTICLE