Hierarchical lung field segmentation with joint shape and appearance sparse learning

Yeqin Shao, Yaozong Gao, Yanrong Guo, Yonghong Shi, Xin Yang, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)


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.

Original languageEnglish
Article number6737258
Pages (from-to)1761-1780
Number of pages20
JournalIEEE Transactions on Medical Imaging
Issue number9
Publication statusPublished - 2014 Sept


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

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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