EWFCM Algorithm and Region-Based Multi-level Thresholding

  • Jun-Taek Oh
  • Wook-Hyun Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


Multi-level thresholding is a method that is widely used in image segmentation. However, most of the existing methods are not suited to be directly used in applicable fields, and moreover they are not extended into a step of image segmentation. This paper proposes region-based multi-level thresholding as an image segmentation method. At first, we classify pixels of each color channel to two clusters by using EWFCM algorithm that is an improved FCM algorithm with spatial information between pixels. To obtain better segmentation results, a reduction of clusters is then performed by a region-based reclassification step based on a similarity between regions existing in a cluster and the other clusters. We finally perform a region merging by Bayesian algorithm based on Kullback-Leibler distance between a region and the neighboring regions as a post-processing method, as many regions still exist in image. Experiments show that region-based multi-level thresholding is superior to cluster-, pixel-based multi-level thresholding, and an existing method and much better segmentation results are obtained by the proposed post-processing method.


Image Segmentation Color Channel Code Image Bayesian Algorithm Image Thresholding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun-Taek Oh
    • 1
  • Wook-Hyun Kim
    • 1
  1. 1.School of EECSYeungnam UniversityGyeongbukSouth Korea

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