Automatic Pathological Lung Segmentation in Low-Dose CT Image Using Eigenspace Sparse Shape Composition

The segmentation of lungs with severe pathology is a nontrivial problem in the clinical application. Due to complex structures, pathological changes, individual differences, and low image quality, accurate lung segmentation in clinical 3-D computed tomography (CT) images is still a challenging task....

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Veröffentlicht in:	IEEE transactions on medical imaging 2019-07, Vol.38 (7), p.1736-1749
Hauptverfasser:	Chen, Geng, Xiang, Dehui, Zhang, Bin, Tian, Haihong, Yang, Xiaoling, Shi, Fei, Zhu, Weifang, Tian, Bei, Chen, Xinjian
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Composition Computed tomography Deformation mechanisms Dictionaries discriminative appearance dictionary eigenspace sparse shape composition Eigenvectors gradient vector flow Humans Image Interpretation, Computer-Assisted - methods Image processing Image quality Image reconstruction Image segmentation Lesions Lung Lung - diagnostic imaging Lung Neoplasms - diagnostic imaging Lungs Medical imaging Pathological lung segmentation Pathology Shape Shape recognition Strain Three-dimensional displays Tomography, X-Ray Computed - methods Tumors
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