Title: Automatic segmentation of multi-class images with NLS model

Authors: K. Punnam Chandar; T. Satya Savithri; B. Swarnalatha

Addresses: Department of Electronics and Communication Engineering, Kakatiya University, Telangana, India ' Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Telangana, Hyderabad, India ' Department of Electronics and Communication Engineering, Kakatiya University, Telangana, India

Abstract: In this paper, automatic segmentation of multi-class images problem is considered. The 1D histogram of the multi-class image is approximated using Gaussian functions and the unknown parameters of the Gaussian functions are estimated using Non-linear Least Squares (NLS) optimisation; thereby the problem of segmentation of unknown image class is modelled as an optimisation problem. Further, the parameter estimation accuracy is improved by using the Pearson linear correlation coefficient as a regularisation term of the objective function. The experimental results demonstrate the NLS algorithm ability to estimate the parameters of the Gaussian functions and thereby automatically determine the multi-thresholds for segmentation.

Keywords: image segmentation; thresholding; non-linear least squares optimisation; differential evolution optimisation; Gaussian mixtures.

DOI: 10.1504/IJBET.2018.094431

International Journal of Biomedical Engineering and Technology, 2018 Vol.28 No.1, pp.81 - 104

Received: 16 Feb 2016
Accepted: 27 Sep 2016

Published online: 03 Sep 2018 *

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