Iterative image segmentation algorithm utilizing spatial information

Chi Kin Leung, F. K. Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


An iterative image segmentation algorithm that segments an image on a pixel-by-pixel basis is described. The observation information to be utilized is the joint gray level values of the pixel to be segmented and those of its neighborhood pixels. The iterative process is initialized by thresholding the image with Otsu's method. Each pixel is segmented into a class when the a posteriori probability, conditioned on the observation information, that it belongs to this class is maximum. The newly segmented image is employed to re-estimate the a posteriori probabilities and the segmentation process is repeated until there is no further pixel classification change in a particular run. Among those segmented images generated in the iterative process, the best segmented image is chosen according to a maximum entropy criterion. Simulation studies demonstrate that the proposed algorithm can achieve very significant improvement in segmentation performance as compared to the more popular thresholding approach. Furthermore, the performance is neither sensitive to the initial threshold value nor the form of the probability density function of the image. Segmentation of practical images also demonstrates that the proposed algorithm is capable of good segmentation results for real-life images.
Original languageEnglish
Title of host publicationIEEE Region 10 Annual International Conference, Proceedings/TENCON
Number of pages6
Publication statusPublished - 1 Dec 1996
EventProceedings of the 1996 IEEE Region 10 TENCON - Digital Signal Processing Applications Conference. Part 2 (of 2) - Perth, Australia
Duration: 26 Nov 199629 Nov 1996


ConferenceProceedings of the 1996 IEEE Region 10 TENCON - Digital Signal Processing Applications Conference. Part 2 (of 2)

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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