Segmentation of Microscopic Images with NSGA-II

Authors

  • Rocio Ochoa-Montiel Universidad Autónoma de Tlaxcala, Apizaco
  • Carlos Sánchez-López Universidad Autónoma de Tlaxcala, Apizaco
  • Victor Hugo Carbajal-Gómez Universidad Autónoma de Tlaxcala, Apizaco
  • Ever Juárez-Guerra Universidad Autónoma de Tlaxcala, Apizaco

DOI:

https://doi.org/10.13053/cys-22-2-2944

Keywords:

Segmentation, multiobjective evolutionary optimization, microscopic images

Abstract

This paper addresses the problem of multiobjective segmentation on microscopic images by using the evolutionary algorithm NSGA-II. Two objective functions are used at the optimization process: Otsu’s inter-class variance and Shannon’s entropy. A set of 71 images of blood cells are used. From this set, three categories of images are generated: with and without preprocessing, and images with Gaussian noise. Experimental results shown that the use of evolutionary multiobjective techniques like NSGA-II, give satisfactory results in the segmentation for more than one category of images.

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Published

2018-06-29

Issue

Vol. 22 No. 2 (2018): Thematic Issue on Numerical and Evolutionary Optimization (Guest editors: L. Trujillo, Y. Maldonado, O. Schuetze)

Section

Articles of the Thematic Issue

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