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Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning 期刊论文

编号：

6c93fe87-6c99-4dc2-987c-4f28f182d929
作者：

Shao, Yeqin^[4,5,6,7] Gao, Yaozong^[1,2] Guo, Yanrong^[1,2] Shi, Yonghong^[8] Yang, Xin^[4] Shen, Dinggang^[1,2,3]
语种：

English
期刊：

IEEE TRANSACTIONS ON MEDICAL IMAGING ISSN：0278-0062 2014 年 33 卷 9 期 (1761 - 1780) ; SEP
收录：
关键词：

Active shape model chest radiograph deformable segmentation local appearance model local sparse shape composition sparse learning
摘要：

Lung field segmentation in the posterior-anterior (PA) chest radiograph is important for pulmonary disease diagnosis and hemodialysis treatment. Due to high shape variation and boundary ambiguity, accurate lung field segmentation from chest radiograph is still a challenging task. To tackle these challenges, we propose a joint shape and appearance sparse learning method for robust and accurate lung field segmentation. The main contributions of this paper are: 1) a robust shape initialization method is designed to achieve an initial shape that is close to the lung boundary under segmentation; 2) a set of local sparse shape composition models are built based on local lung shape segments to overcome the high shape variations; 3) a set of local appearance models are similarly adopted by using sparse representation to capture the appearance characteristics in local lung boundary segments, thus effectively dealing with the lung boundary ambiguity; 4) a hierarchical deformable segmentation framework is proposed to integrate the scale-dependent shape and appearance information together for robust and accurate segmentation. Our method is evaluated on 247 PA chest radiographs in a public dataset. The experimental results show that the proposed local shape and appearance models outperform the conventional shape and appearance models. Compared with most of the state-of-the-art lung field segmentation methods under comparison, our method also shows a higher accuracy, which is comparable to the inter-observer annotation variation.
推荐引用方式
GB/T 7714：

Shao Yeqin,Gao Yaozong,Guo Yanrong, et al. Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning [J].IEEE TRANSACTIONS ON MEDICAL IMAGING,2014,33(9):1761-1780.
APA：

Shao Yeqin,Gao Yaozong,Guo Yanrong,Shi Yonghong,&Shen Dinggang.(2014).Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning .IEEE TRANSACTIONS ON MEDICAL IMAGING,33(9):1761-1780.
MLA：

Shao Yeqin, et al. "Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning" .IEEE TRANSACTIONS ON MEDICAL IMAGING 33,9(2014):1761-1780.

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