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U. Gonzales-Baron and F. Butler, “A Comparison of Seven Thresholding Techniques with the K-Means Clus-tering Algorithm for Measurement of Bread-Crumb Fea-tures by Digital Image Analysis,” Journal of Food Engi-neering, Vol. 74, No. 2, 2006, pp. 268-278.

has been cited by the following article:

• TITLE: A New Multilevel Thresholding Method Using Swarm Intelligence Algorithm for Image Segmentation

  AUTHORS: Sathya P. Duraisamy, Ramanujam Kayalvizhi

  KEYWORDS: Image Segmentation, Multilevel Thresholding, Particle Swarm Optimization

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.2 No.3, September 8, 2010

  ABSTRACT: Thresholding is a popular image segmentation method that converts gray-level image into binary image. The selection of optimum thresholds has remained a challenge over decades. In order to determine thresholds, most methods analyze the histogram of the image. The optimal thresholds are often found by either minimizing or maximizing an objective function with respect to the values of the thresholds. In this paper, a new intelligence algorithm, particle swarm opti-mization (PSO), is presented for multilevel thresholding in image segmentation. This algorithm is used to maximize the Kapur’s and Otsu’s objective functions. The performance of the PSO has been tested on ten sample images and it is found to be superior as compared with genetic algorithm (GA).