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Active shape dictionary for automatic segmentation of pathological lung in low-dose CT image
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Paper Abstract

Accurate lung segmentation is of great significance in clinical application. However, it is still a challenging task due to its complex structures, pathological changes, individual differences and low image quality. In the paper, a novel shape dictionary-based approach, named active shape dictionary, is introduced to automatically delineate pathological lungs from clinical 3D CT images. The active shape dictionary improves sparse shape composition in eigenvector space to effectively reduce local shape reconstruction error. The proposed framework makes the shape model to be iteratively deformed to target boundary with discriminative appearance dictionary learning and gradient vector flow to drive the landmarks. The proposed algorithm is tested on 40 3D low-dose CT images with lung tumors. Compared to state-of-the-art methods, the proposed approach can robustly and accurately detect pathological lung surface.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094926 (15 March 2019); doi: 10.1117/12.2511215
Show Author Affiliations
Geng Chen, Soochow Univ. (China)
Dehui Xiang, Soochow Univ. (China)
Haihong Tian, Soochow Univ. (China)
Bin Zhang, The First Affiliated Hospital of Soochow Univ. (China)
Weifang Zhu, Soochow Univ. (China)
Fei Shi, Soochow Univ. (China)
Xinjian Chen, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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