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Automatic Lung Segmentation using Hybrid Approach  

Yim, Yeny (서울대학교 컴퓨터공학부)
Hong, Helen (서울대학교 컴퓨터공학부)
Shin, Yeong-Gil (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.7, 2005 , pp. 625-635 More about this Journal
Abstract
In this paper, we propose a hybrid approach for segmenting the lungs efficiently and automatically in chest CT images. The proposed method consists of the following three steps. first, lungs and airways are extracted by two- and three-dimensional automatic seeded region growing and connected component labeling in low-resolution. Second, trachea and large airways are delineated from the lungs by two-dimensional morphological operations, and the left and right lungs are identified by connected component labeling in low-resolution. Third, smooth and accurate lung region borders are obtained by refinement based on image subtraction. In experiments, we evaluate our method in aspects of accuracy and efficiency using 10 chest CT images obtained from 5 patients. To evaluate the accuracy, we Present results comparing our automatic method to manually traced borders from radiologists. Experimental results show that proposed method which use connected component labeling in low-resolution reduce processing time by 31.4 seconds and maximum memory usage by 196.75 MB on average. Our method extracts lung surfaces efficiently and automatically without additional processing like hole-filling.
Keywords
Image Segmentation; Seeded Region Growing; Connected Component Labeling; Morphological Operations;
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Times Cited By KSCI : 1  (Citation Analysis)
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