Lung Fields Segmentation Algorithm in Chest Radiography - Advances in Image and Graphics Technologies - page 144

6 Conclusion

In this paper, we proposed a segmentation algorithm based on feature images, gray
and shape cost, which can improve the performance of the segmentation of lung out-
line. Moreover, modification of the lung outline based on ASM was employed to
overcome the limits of the search regions not covered the real lung fields, and further
improved the performance of the segmentation of lung fields.

Acknowledgements. This work was supported in part by National Natural Science
Foundation of China (No.61373088), the PhD Start-up Fund of National Science
Foundation of Liaoning Province (No.20131086), National Aerospace Science Foun-
dation (No.2013ZE54025), Shenyang Science and Technology Foundation (No.F13-
316-1-35), and the PhD Start-up Fund of SAU (No.13YB16).

References

1. Cancer Facts and Figures 2013. The American Cancer Society, Atlanta (2013)
2. Li, Y., Dai, M., Chen, L., Zhang, S., Chen, W., Dai, Z., et al.: Study on the Estimation of
Lung Cancer Mortality of Provincial Level Chinese. China Oncology, 120-126 (2011)
3. Lei, T.: The Ten Most Common Cancer Mortality and Composition in China. China Can-
cer 12, 801-802 (2010)
4. The International Early Lung Cancer Action Program Investigators Survival of Patients
with Stage I Lung Cancer Detected on CT Screening. The New England Journal of Medi-
cal 355, 1763-1771 (2006)
5. Xu, X.X., Dio, K.: Image Feature Analysis for Computer-aided Diagnosis: Detection of
Right and Left Hemidiaphragm Edges and Delineation of Lung Field in Chest Radio-
graphs. Medical Physics 23, 1613-1624 (1996)
6. Ginneken, B., Stegmann, M., Loog, M.: Segmentation of anatomical structures in chest
radiographs using supervised methods: a comparative study on a public database. Med.
Image Anal. 10, 19-40 (2006)
7. Shi, Y., Qi, F., Xue, Z., Chen, L., Ito, K., Matsuo, H., Shen, D.: Segmenting lung fields in
serial chest radiographs using both population-based and patient-specific shape statistics.
IEEE Transactions on Medical Imaging 27, 481-494 (2008)
8. Soleymanpour, E., Pourreza, H.R., Ansaripour, E., Yazdi, M.: Fully automatic lung seg-
mentation and rib suppression methods to improve nodule detection in chest radiographs.
J. Med. Signals Sens. 1, 191-199 (2011)
9. Shi, Z., Zhou, P., He, L., Nakamura, T., Yao, Q., Itoh, H.: Lung segmentation in chest ra-
diographs by means of Gaussian kernel-based FCM with spatial constraints. In: ICNC-
FSKS, Tianjin, China (2009)
10. Liu, Y., Qiu, T., Guo, D.: The lung segmentation in chest radiographs based on the flexible
morphology and clustering algorithm. Chinese Journal of Biomedical Engineering 26,
684-689 (2007)
11. Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models — their training and
applications. Computer Vision Image Understand 61, 38-59 (1995)
12. Zhang, Y., Chen, X., Hao, X., Xia, S.: Dynamic programming algorithm for pulmonary
module CTimages based on counter supervision. Chinese Journal of Scientific Instru-
ment 33, 25-27 (2012)