Title: Sustainable analysis of liver tumour detection using various segmentation techniques

Authors: Reshma Jose; Shanty Chacko

Addresses: Department of ECE, Karunya Institute of Technology and Sciences, Karunyanagar, Coimbatore-641114, India ' Department of EEE, Karunya Institute of Technology and Sciences, Karunyanagar, Coimbatore-641114, India

Abstract: The death rate of liver cancer disease is the most astounding among every other kind of tumour. Survival from liver disease is specifically identified with its development at its discovery time. Early recognition of liver malignancy is the most encouraging approach to reduce the risk for survival. Staging of cancer at its investigation is the major predictor of survival, and it determines the treatment. The proposed system mainly focuses to detect the segmentation region of the liver tumour, the proposed algorithm of preprocessing is performed in histogram equalisation and the segmentation region is detected by using Otsu's segmentation, fuzzy C-means clustering and region-based active contour method. The research gap for the base paper is to find the tumour region in the liver. Simultaneous occurrence of more than one primary nodule in the liver region leads to the malignant stage.

Keywords: histogram equalisation; Otsu's segmentation; fuzzy C-mean clustering; region-based active contour.

DOI: 10.1504/WRSTSD.2021.114682

World Review of Science, Technology and Sustainable Development, 2021 Vol.17 No.2/3, pp.236 - 247

Received: 16 Nov 2019
Accepted: 28 Jul 2020

Published online: 30 Apr 2021 *

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