AUTOMATIC LIVER TISSUE SECTION IMAGE CHARACTERISTICS

Neelakantesh P; Chandra Vani D; CHANDANA M; Dr I Suneetha; NARENDRA D

AUTOMATIC LIVER TISSUE SECTION IMAGE CHARACTERISTICS

Authors: Neelakantesh P, Chandra Vani D, CHANDANA M, Dr I Suneetha, NARENDRA D

Unique Paper ID: 152050
Volume: 8
Issue: 2
PageNo: 236-242

Keywords: Microscopy image processing cell counting nuclei segmentation vessel classification SVM canny edge detection Hough transform.

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.

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Cite This Article

ISSN: 2349-6002
Volume: 8
Issue: 2
PageNo: 236-242

AUTOMATIC LIVER TISSUE SECTION IMAGE CHARACTERISTICS

Available:https://ijirt.org/Article?manuscript=152050

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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