Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 874761, 20 pages
Research Article

Multithreshold Segmentation Based on Artificial Immune Systems

1Departamento de Ciencias Computacionales, CUCEI, Universidad de Guadalajara, Avenida Revolución 1500, 44430 Guadalajara, JAL, Mexico
2Centro de Investigación en Computación-IPN, Avenida Juan de Dios Bátiz s/n, Colonia Nueva Industrial Vallejo, 07738 Mexico, DF, Mexico

Received 19 February 2012; Accepted 18 April 2012

Academic Editor: Yi-Chung Hu

Copyright © 2012 Erik Cuevas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS) technique, also known as theclonal selection algorithm (CSA). It follows the clonal selection principle (CSP) from the human immune system which basically generates a response according to the relationship between antigens (Ag), that is, patterns to be recognized and antibodies (Ab), that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM) algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms.