Image Thresholding Using Differential Evolution

Image thresholding is a challenging task in image processing field. Many efforts have already been made to propose universal, robust methods to handle a wide range of images. This paper introduces a new optimization-based thresholding approach. The optimizer, Differential Evolution (DE) algorithm, minimizes dissimilarity between the input grey-level image and the bi-level (thresholded) image. The proposed approach is compared with a well-known thresholding method, Kittler algorithm, through subjective and objective assessments, and experimental results are provided.

[1]  Shahryar Rahnamayan,et al.  Automated Snake Initialization for the Segmentation of the Prostate in Ultrasound Images , 2005, ICIAR.

[2]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution Algorithms , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[3]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[4]  Godfrey C. Onwubolu,et al.  New optimization techniques in engineering , 2004, Studies in Fuzziness and Soft Computing.

[5]  Stefan Winkler,et al.  Vision models and quality metrics for image processing applications , 2001 .

[6]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[7]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[8]  Jean-Bernard Martens,et al.  Image dissimilarity , 1998, Signal Process..

[9]  Kenneth V. Price,et al.  An introduction to differential evolution , 1999 .

[10]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution for Optimization of Noisy Problems , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[11]  M. Ibrahim Sezan,et al.  A Peak Detection Algorithm and its Application to Histogram-Based Image Data Reduction , 1990, Comput. Vis. Graph. Image Process..

[12]  William A. Yasnoff,et al.  Error measures for scene segmentation , 1977, Pattern Recognit..

[13]  Amit Konar,et al.  Improved differential evolution algorithms for handling noisy optimization problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[14]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .