Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics

Yonghong Shi, Feihu Qi, Zhong Xue, Liya Chen, Kyoko Ito, Hidenori Matsuo, Dinggang Shen

Research output: Contribution to journalArticle

106 Citations (Scopus)

Abstract

This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. There are two novelties in the proposed deformable model. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel. Second, the deformable contour is constrained by both population-based and patient-specific shape statistics, and it yields more robust and accurate segmentation of lung fields for serial chest radiographs. In particular, for segmenting the initial time-point images, the population-based shape statistics is used to constrain the deformable contour; as more subsequent images of the same patient are acquired, the patient-specific shape statistics online collected from the previous segmentation results gradually takes more roles. Thus, this patient-specific shape statistics is updated each time when a new segmentation result is obtained, and it is further used to refine the segmentation results of all the available time-point images. Experimental results show that the proposed method is more robust and accurate than other active shape models in segmenting the lung fields from serial chest radiographs.

Original languageEnglish
Article number4359073
Pages (from-to)481-494
Number of pages14
JournalIEEE Transactions on Medical Imaging
Volume27
Issue number4
DOIs
Publication statusPublished - 2008 Apr
Externally publishedYes

Keywords

  • Deformable model
  • Scale invariant feature transform (SIFT) local descriptor
  • Segmentation
  • Serial chest radiographs
  • Shape statistics

ASJC Scopus subject areas

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

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