21 July 2017 Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202G (2017) https://doi.org/10.1117/12.2281942
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
While the popular thin layer scanning technology of spiral CT has helped to improve diagnoses of lung diseases, the large volumes of scanning images produced by the technology also dramatically increase the load of physicians in lesion detection. Computer-aided diagnosis techniques like lesions segmentation in thin CT sequences have been developed to address this issue, but it remains a challenge to achieve high segmentation efficiency and accuracy without much involvement of human manual intervention. In this paper, we present our research on automated segmentation of lung parenchyma with an improved geodesic active contour model that is geodesic active contour model based on similarity (GACBS). Combining spectral clustering algorithm based on Nystrom (SCN) with GACBS, this algorithm first extracts key image slices, then uses these slices to generate an initial contour of pulmonary parenchyma of un-segmented slices with an interpolation algorithm, and finally segments lung parenchyma of un-segmented slices. Experimental results show that the segmentation results generated by our method are close to what manual segmentation can produce, with an average volume overlap ratio of 91.48%.
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Nana He, Nana He, Xiaolong Zhang, Xiaolong Zhang, Juanjuan Zhao, Juanjuan Zhao, Huilan Zhao, Huilan Zhao, Yan Qiang, Yan Qiang, } "Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity ", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202G (21 July 2017); doi: 10.1117/12.2281942; https://doi.org/10.1117/12.2281942
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