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

Geng Chen; Dehui Xiang; Haihong Tian; Bin Zhang; Weifang Zhu; Fei Shi; Xinjian Chen

doi:10.1117/12.2511215

15 March 2019 Active shape dictionary for automatic segmentation of pathological lung in low-dose CT image

Geng Chen, Dehui Xiang, Haihong Tian, Bin Zhang, Weifang Zhu, Fei Shi, Xinjian Chen

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 1094926 (2019) https://doi.org/10.1117/12.2511215
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Geng Chen, Dehui Xiang, Haihong Tian, Bin Zhang, Weifang Zhu, Fei Shi, and Xinjian Chen "Active shape dictionary for automatic segmentation of pathological lung in low-dose CT image", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094926 (15 March 2019); https://doi.org/10.1117/12.2511215

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KEYWORDS

Lung

Image segmentation

3D modeling

Computed tomography

Associative arrays

3D image processing

Tumors

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