26 June 1992 Three-dimensional adaptive split-and-merge method for medical image segmentation
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Abstract
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.
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Jin-Shin Chou, Chin-Tu Chen, Shiuh-Yung James Chen, Wei-Chung Lin, "Three-dimensional adaptive split-and-merge method for medical image segmentation", Proc. SPIE 1660, Biomedical Image Processing and Three-Dimensional Microscopy, (26 June 1992); doi: 10.1117/12.59573; https://doi.org/10.1117/12.59573
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