DocumentCode :
2519503
Title :
Segmentation of Lung Region for Chest X-Ray Images Based on Medical Registration And ASM
Author :
Wang, Chunyan ; Guo, Shengwen ; Wu, Xiaoming
Author_Institution :
Dept. of Biomed. Eng., South China Univ. of Technol., Guangzhou, China
fYear :
2009
fDate :
11-13 June 2009
Firstpage :
1
Lastpage :
4
Abstract :
It´s very important to locate and recognize the lung region accurately in chest X-ray images in clinical application and research. This paper provides a novel method to extract the lung region in the chest X-ray images. In this paper, the active shape model (ASM) based on deformed technology is applied to segment the lung region. In order to get a more accurate and time-saving segmentation result, we also improve the original ASM. Firstly, Because of the body location and the individual difference, we use thin-plate spline method to register the chest radiographs in order to get a more appropriate shape model .Secondly, apex pulmonis and gulus costa are localized and used to initialize the mean shape mode, and the mean intensity of the bound rectangle regions of the initial shapes is adopted to match the shapes. Finally, a multi-resolution framework based on Gaussian pyramid was introduced to acquire quick iteration. Experimental results show that our algorithm performs significantly better than the original ASM.
Keywords :
biological organs; diagnostic radiography; image registration; image resolution; image segmentation; iterative methods; lung; medical image processing; ASM; X-ray images; active shape model; apex pulmonis; chest radiographs; gulus costa; lung region segmentation; medical image registration; multiresolution framework; thin-plate spline method; Active shape model; Biomedical engineering; Biomedical imaging; Image recognition; Image segmentation; Lungs; Radiography; Shape control; Spline; X-ray imaging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2901-1
Electronic_ISBN :
978-1-4244-2902-8
Type :
conf
DOI :
10.1109/ICBBE.2009.5163372
Filename :
5163372
Link To Document :
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