Abstract
Accurate segmentation of lung fields in chest radiography is an essential part of computer-aided detection. We proposed a segmentation method by use of feature images, gray and shape cost, and modification method. The outline of lung fields in the training set was marked and aligned to create an initial outline. Then, dynamic program was employed to determine the optimal one in terms of the gray and shape cost in the six feature images. Finally, the lung outline was modified by Active Shape Model. The experimental results show that the average segmentation overlaps without and with feature images achieve 82.18% and 89.07%, respectively. After the modification of segmentation, the average overlap can reach 90.26%.
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References
Cancer Facts and Figures 2013. The American Cancer Society, Atlanta (2013)
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)
Lei, T.: The Ten Most Common Cancer Mortality and Composition in China. China Cancer 12, 801–802 (2010)
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 Medical 355, 1763–1771 (2006)
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 Radiographs. Medical Physics 23, 1613–1624 (1996)
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)
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)
Soleymanpour, E., Pourreza, H.R., Ansaripour, E., Yazdi, M.: Fully automatic lung segmentation and rib suppression methods to improve nodule detection in chest radiographs. J. Med. Signals Sens. 1, 191–199 (2011)
Shi, Z., Zhou, P., He, L., Nakamura, T., Yao, Q., Itoh, H.: Lung segmentation in chest radiographs by means of Gaussian kernel-based FCM with spatial constraints. In: ICNC-FSKS, Tianjin, China (2009)
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)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models — their training and applications. Computer Vision Image Understand 61, 38–59 (1995)
Zhang, Y., Chen, X., Hao, X., Xia, S.: Dynamic programming algorithm for pulmonary module CTimages based on counter supervision. Chinese Journal of Scientific Instrument 33, 25–27 (2012)
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Zhang, G., Cong, L., Wang, L., Guo, W. (2014). Lung Fields Segmentation Algorithm in Chest Radiography. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Huang, K. (eds) Advances in Image and Graphics Technologies. IGTA 2014. Communications in Computer and Information Science, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45498-5_16
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DOI: https://doi.org/10.1007/978-3-662-45498-5_16
Publisher Name: Springer, Berlin, Heidelberg
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