Symmetric region growing

被引:123
作者
Wan, SY [1 ]
Higgins, WE [1 ]
机构
[1] Penn State Univ, Dept Elect Engn, University Pk, PA 16802 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
connected-components analysis; image segmentation; region growing; region-based segmentation; three-dimensional image analysis;
D O I
10.1109/TIP.2003.815258
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Of the many proposed image-segmentation methods, region growing has been one of the most popular. Research on region growing, however, has focused primarily on the design of feature measures and on growing and merging criteria. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points. We define a set of theoretical criteria for a subclass of region-growing algorithms that are insensitive to the selection of the initial growing points. This class of algorithms, referred to as Symmetric Region Growing, leads to a single-pass region-growing algorithm applicable to any dimensionality of images. Furthermore, they lead to region-growing algorithms that are both memory- and computation-efficient. Results illustrate the method's efficiency and its application to 3-D medical image segmentation.
引用
收藏
页码:1007 / 1015
页数:9
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