Three-dimensional adaptive split-and-merge method for medical image segmentation

Jin Shin Chou*, Chin Tu Chen, Shiuh Yung Chen, Wei Chung Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


We have developed a three-dimensional image segmentation algorithm using adaptive split-and-merge method. The framework of this method is based on a two-dimensional (2- D) split-and-merge scheme and the region homogeneity analysis. Hierarchical oct-tree is used as the basic data structure throughout the analysis, analogous to quad-tree in the 2-D case. A localized feature analysis and statistical tests are employed in the testing of region homogeneity. In feature analysis, standard deviation, gray-level contrast, likelihood ratio, and their corresponding co-occurrence matrix are computed. Histograms of the near- diagonal elements of the co-occurrence matrix are calculated. An optimal thresholding method is then applied to determine the desired threshold values. These values are then used as constraints in the tests, such that decision of splitting or merging can be made.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Number of pages7
Editionpt 1
ISBN (Print)081940814X
StatePublished - Dec 1 1992
EventBiomedical Image Processing and Three-Dimensional Microscopy. Part 1 (of 2) - San Jose, CA, USA
Duration: Feb 10 1991Feb 13 1991


OtherBiomedical Image Processing and Three-Dimensional Microscopy. Part 1 (of 2)
CitySan Jose, CA, USA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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