Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{152050,
        author = {Neelakantesh P and Chandra Vani D and CHANDANA M and Dr I Suneetha and NARENDRA D},
        title = {AUTOMATIC LIVER TISSUE SECTION IMAGE CHARACTERISTICS},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {2},
        pages = {236-242},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152050},
        abstract = {Analyzing liver cross-section images is a manual and laborious task. Liver transplantation is currently the only cure for patients with end-stage liver disease. Alternating treatment options are critically needed for these patients. Understanding the molecular mechanisms by which the proliferation of an important type of liver cell called hepatocytes is regulated. The methods to automatically count hepatocytes cells, count nuclei and classify liver vessel types, using input images of cell boundary and cell nuclei based on a dataset of 21 images. Compared to a trained researcher, the methods are able to count cells, segment overlapping nuclei, and classify vessel types, including portal vein, central vein, and bile duct with reasonable precision and accuracy and detecting other cell types. All the methods are categorized into three main classes including gray level-based method, structure based-method and texture based-method.},
        keywords = {Microscopy image processing, cell counting, nuclei segmentation, vessel classification, SVM, canny edge detection, Hough transform.},
        month = {},
        }