Research on Segmentation Method of Lung with Juxta-pleural Tumor Based on the Improved Active Shape Model_Journal of Biomedical Engineering

Authors：

 SUNShenshen ¹ , FANLinan ¹ , KANGYan ² , RENHuizhi ³ , QIShouliang ²

1. School of Information and Engineering, Shenyang University, Shenyang 110044, China;
2. School of Sino-Dutch Biomedical and Information Engineering, Northeastern University, Shenyang 110819, China;
3. School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China;

Corresponding author：

SUNShenshen, Email: sunshenshen@126.com

Keywords：

lung with juxta-pleural tumor; active shape model; lung segmentation; outlier marker point; searching function

DOI：

10.7507/1001-5515.20160142

Video：

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Abstract Full text Figures/Tables Video References Cited by

Lung segmentation is the premise of the computer aided diagnosis of lung cancer. The traditional segmentation method based on local low-level features can not get the correct result when a tumor is connected with pleura due to their similar computed tomography (CT) values. Moreover, because the big size of tumor leads to the loss of a large part of lung area, the traditional segmentation methods of lung with juxta-pleural nodule whose diameter is less than 3 cm are not suitable. Acitve shape model (ASM) combined with prior shape and low level features might be appropriate. But the search steps in conventional ASM is an optimization method based on the least square, which is sensitive to outlier marker points, and it makes profile update to the transition area of normal lung tissue and tumor rather than a true lung contour. To solve the problem, we proposed an improved ASM algorithm. Firstly, we identified these outlier marker points by distance, and then gave the different searching functions to the abnormal and normal marker points. And the search processing should be limited in volume of interesting (VOI). We selected 30 lung images with juxta-pleural tumors, and got the overlap rate with the gold standard as 93.6%. The experimental results showed that the improved ASM could get good segmentation results for the lungs with juxta-pleural tumors, and the running time of the algorithm could be tolerated in clinical.

Citation： SUN Shenshen, FAN Linan, KANG Yan, REN Huizhi, QI Shouliang. Research on Segmentation Method of Lung with Juxta-pleural Tumor Based on the Improved Active Shape Model. Journal of Biomedical Engineering, 2016, 33(5): 879-884. doi: 10.7507/1001-5515.20160142 Copy

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4.	PU Jiantao, PAIK D S, MENG Xin, et al. Shape "break-and-repair" strategy and its application to automated medical image segmentation[J]. IEEE Trans Vis Comput Graph, 2011, 17(1):115-124.
5.	SLUIMER I, PROKOP M, VAN GINNEKEN B. Toward automated segmentation of the pathological lung in CT[J]. IEEE Trans Med Imaging, 2005, 24(8):1025-1038.
6.	FARAG A A, EL MUNIM H E, GRAHAM J H, et al. A novel approach for lung nodules segmentation in chest CT using level sets[J]. IEEE Trans Image Process, 2013, 22(12):5202-5213.
7.	ROGERS M, GRAHAM J. Robust active shape model search[C]//Proceedings of the European Conference on Computer Vision, Denmark. Berlin Heidelberg:Springer Berlin Heidelberg, 2002, 1(1):517-530.
8.	LEKADIR K, MERRIFIELD R, YANG Guangzhong. Outlier detection and handling for robust 3-D active shape models search[J]. IEEE Trans Med Imaging, 2007, 26(2):212-222.
9.	SUN S, BAUER C, BEICHEL R. Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach[J]. IEEE Trans Med Imaging, 2012, 31(2):449-460.
10.	COOTES T F, TAYLOR C J, COOPER D H, et al. Active shape models——Their training and application[J]. Computer Vision and Image Understanding, 1995, 61(1):38-59.
11.	HU S, Hoffman E A, REINHARDT J M. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images[J]. IEEE Trans Med Imaging, 2001, 20(6):490-498.

1. KOSTIS W J, REEVES A P, YANKELEVITZ D F, et al. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images[J]. IEEE Trans Med Imaging, 2003, 22(10):1259-1274.
2. WAY T W, HADJIISKI L M, SAHINER B, et al. Computer-aided diagnosis of pulmonary nodules on CT scans:Segmentation and classification using 3D active contours[J]. Med Phys, 2006, 33(7):2323-2337.
3. KITASAKA T, MORI K, HASEGAWA J, et al. Lung area extraction from 3-D chest X-ray CT images using a shape model generated by a variable Bézier surface[J]. Syst Comput Jpn, 2003, 34(4):60-71.
4. PU Jiantao, PAIK D S, MENG Xin, et al. Shape "break-and-repair" strategy and its application to automated medical image segmentation[J]. IEEE Trans Vis Comput Graph, 2011, 17(1):115-124.
5. SLUIMER I, PROKOP M, VAN GINNEKEN B. Toward automated segmentation of the pathological lung in CT[J]. IEEE Trans Med Imaging, 2005, 24(8):1025-1038.
6. FARAG A A, EL MUNIM H E, GRAHAM J H, et al. A novel approach for lung nodules segmentation in chest CT using level sets[J]. IEEE Trans Image Process, 2013, 22(12):5202-5213.
7. ROGERS M, GRAHAM J. Robust active shape model search[C]//Proceedings of the European Conference on Computer Vision, Denmark. Berlin Heidelberg:Springer Berlin Heidelberg, 2002, 1(1):517-530.
8. LEKADIR K, MERRIFIELD R, YANG Guangzhong. Outlier detection and handling for robust 3-D active shape models search[J]. IEEE Trans Med Imaging, 2007, 26(2):212-222.
9. SUN S, BAUER C, BEICHEL R. Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach[J]. IEEE Trans Med Imaging, 2012, 31(2):449-460.
10. COOTES T F, TAYLOR C J, COOPER D H, et al. Active shape models——Their training and application[J]. Computer Vision and Image Understanding, 1995, 61(1):38-59.
11. HU S, Hoffman E A, REINHARDT J M. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images[J]. IEEE Trans Med Imaging, 2001, 20(6):490-498.

Journal of Biomedical Engineering

Research on Segmentation Method of Lung with Juxta-pleural Tumor Based on the Improved Active Shape Model

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