Iterative multi-level image segmentation based on fuzzy set theory

Yu Qian Zhao*, Yuan Yang, Kun Wang

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

To solve low accuracy and time-consuming problems in thresholding, an iterative multi-level image segmentation method based on fuzzy set theory was proposed. The interactive top-down region dividing strategy was used considering characteristics of gray level distribution and neighborhood information of each pixel in the pre-dividing region. And fuzzy comprehensive evaluation was used instead of segmentation thresholds to classify each pixel in the image reasonably. The method overcomes the limitations and complexity in thresholding, and improves the quality and efficiency of image segmentation. To test the superiority and rapidity of presented method, a set of noisy images and different contrast images, along with two representative complex images, were segmented. And two classical thresholding methods, Otsu's method and Kapur's method, were used as comparisons. The experimental results show that the presented method costs shorter time and preserves detail information better for the concerned.

Original languageEnglish (US)
Pages (from-to)1403-1409
Number of pages7
JournalGuangdianzi Jiguang/Journal of Optoelectronics Laser
Volume20
Issue number10
StatePublished - Oct 1 2009

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Fuzzy set theory
Image segmentation
Pixels
Costs

Keywords

  • Comprehensive evaluation
  • Fuzzy set theory
  • Gray histogram
  • Multi-level segmentation
  • Thresholding

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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title = "Iterative multi-level image segmentation based on fuzzy set theory",
abstract = "To solve low accuracy and time-consuming problems in thresholding, an iterative multi-level image segmentation method based on fuzzy set theory was proposed. The interactive top-down region dividing strategy was used considering characteristics of gray level distribution and neighborhood information of each pixel in the pre-dividing region. And fuzzy comprehensive evaluation was used instead of segmentation thresholds to classify each pixel in the image reasonably. The method overcomes the limitations and complexity in thresholding, and improves the quality and efficiency of image segmentation. To test the superiority and rapidity of presented method, a set of noisy images and different contrast images, along with two representative complex images, were segmented. And two classical thresholding methods, Otsu's method and Kapur's method, were used as comparisons. The experimental results show that the presented method costs shorter time and preserves detail information better for the concerned.",
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Iterative multi-level image segmentation based on fuzzy set theory. / Zhao, Yu Qian; Yang, Yuan; Wang, Kun.

In: Guangdianzi Jiguang/Journal of Optoelectronics Laser, Vol. 20, No. 10, 01.10.2009, p. 1403-1409.

Research output: Contribution to journalArticle

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AU - Zhao, Yu Qian

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AU - Wang, Kun

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N2 - To solve low accuracy and time-consuming problems in thresholding, an iterative multi-level image segmentation method based on fuzzy set theory was proposed. The interactive top-down region dividing strategy was used considering characteristics of gray level distribution and neighborhood information of each pixel in the pre-dividing region. And fuzzy comprehensive evaluation was used instead of segmentation thresholds to classify each pixel in the image reasonably. The method overcomes the limitations and complexity in thresholding, and improves the quality and efficiency of image segmentation. To test the superiority and rapidity of presented method, a set of noisy images and different contrast images, along with two representative complex images, were segmented. And two classical thresholding methods, Otsu's method and Kapur's method, were used as comparisons. The experimental results show that the presented method costs shorter time and preserves detail information better for the concerned.

AB - To solve low accuracy and time-consuming problems in thresholding, an iterative multi-level image segmentation method based on fuzzy set theory was proposed. The interactive top-down region dividing strategy was used considering characteristics of gray level distribution and neighborhood information of each pixel in the pre-dividing region. And fuzzy comprehensive evaluation was used instead of segmentation thresholds to classify each pixel in the image reasonably. The method overcomes the limitations and complexity in thresholding, and improves the quality and efficiency of image segmentation. To test the superiority and rapidity of presented method, a set of noisy images and different contrast images, along with two representative complex images, were segmented. And two classical thresholding methods, Otsu's method and Kapur's method, were used as comparisons. The experimental results show that the presented method costs shorter time and preserves detail information better for the concerned.

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