Enhancing Segmentation Approaches From Gaussian Mixture Model and Expected Maximization to Super Pixel Division Algorithm

Ananth, Christo; Brabin, D.R.Denslin

doi:10.2139/ssrn.3570473

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Enhancing Segmentation Approaches From Gaussian Mixture Model and Expected Maximization to Super Pixel Division Algorithm

SYLWAN., 164(4), ISI Indexed, April 2020

18 Pages Posted: 3 May 2020

See all articles by Christo Ananth

D.R.Denslin Brabin

Madanapalle Institute of Technology and Science, Andhra Pradesh, India

Date Written: 2020

Abstract

Automatic liver tumor segmentation would greatly influence liver treatment organizing strategy and follow-up assessment, as a result of organization and joining of full picture information. Right now, develop a totally programmed technique for liver tumor division in CT picture. Introductory liver division comprises of applying a functioning form strategy. In the wake of separating liver applying Super pixel division Algorithm for portioning liver tumor proficiently. In the proposed work, we will investigate these procedures so as to improve division of various segments of the CT pictures. The exploratory outcomes indicated that the proposed strategy was exact for liver tumor division.

Keywords: Active Pixel, Gaussian Mixture Model, Cellular Neural Networks, Level Set, Expectation-Maximization Algorithm

Suggested Citation: Suggested Citation

Ananth, Christo and Brabin, D.R.Denslin, Enhancing Segmentation Approaches From Gaussian Mixture Model and Expected Maximization to Super Pixel Division Algorithm (2020). SYLWAN., 164(4), ISI Indexed, April 2020 , Available at SSRN: https://ssrn.com/abstract=3570473 or http://dx.doi.org/10.2139/ssrn.3570473