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Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding

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  • Ouarda Assas

    (Department of Computer Science, University of M'sila, M'sila, Algeria)

Abstract

Thresholding is a fundamental task and a challenge for many image analysis and pre-processing process. However, the automatic selection of an optimum threshold has remained a challenge in image segmentation. The fuzzy 2-partition entropy approach for threshold selection is one of the best image thresholding techniques. In this work, an improvement of the later method using type-2 fuzzy sets is proposed to represent the imprecision or lack of knowledge of the expert in the choice of the membership function associated with the image. Two databases are used to evaluate its effectiveness: dataset of standard grayscale test images and MR Brain images. Experiment results show that the type-2 Fuzzy 2-partition entropy algorithm performs equally well in terms of the quality of image segmentation and leads to a good visual result.

Suggested Citation

  • Ouarda Assas, 2015. "Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 6(3), pages 33-48, July.
    • Handle: RePEc:igg:jaec00:v:6:y:2015:i:3:p:33-48
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