Automatic Segmentation of Lung Fields from Radiographic Images of SARS Patients Using a New Graph Cuts Algorithm

This paper proposes an approach to the segmentation of lung fields in the severe acute respiratory syndrome (SARS) infected radiographic images, which is the first step towards a computer-aided diagnosis system. To overcome the segmentation difficulty of highly atypical property of SARS in the lung...

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Bibliographic Details
Published in18th International Conference on Pattern Recognition (ICPR'06) Vol. 1; pp. 271 - 274
Main Authors Shifeng Chen, Liangliang Cao, Jianzhuang Liu, Xiaoou Tang
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Active contours
Computer aided diagnosis
Diagnostic radiography
Humans
Image segmentation
Immune system
Lungs
Morphological operations
Optimization methods
Pixel
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ISBN0769525210
9780769525211
ISSN1051-4651
DOI10.1109/ICPR.2006.304

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Summary:This paper proposes an approach to the segmentation of lung fields in the severe acute respiratory syndrome (SARS) infected radiographic images, which is the first step towards a computer-aided diagnosis system. To overcome the segmentation difficulty of highly atypical property of SARS in the lung images, our algorithm first uses morphological operations to obtain the initial estimation of the regions where the lung boundaries lie in, and then applies a new graph-based optimization method to find the interested regions. The theoretical analysis shows that our approach is resistant to boundary discontinuity, noise, and large patches that affect the boundary search. Experimental results are given to demonstrate the good performance of our algorithm
ISBN:0769525210
9780769525211
ISSN:1051-4651
DOI:10.1109/ICPR.2006.304