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

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

Research output: Chapter in Book/Report/Conference proceedingChapter

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. First, a modified scale-invariant feature transform (SIFT) local descriptor is used to characterize the image features in the vicinity of each pixel, so that the deformable model deforms in a way that seeks for the region with similar SIFT local descriptors. Second, the deformable model is constrained by both population-based and patient-specified shape statistics. Initially, population-based shape statistics takes most of the rules when the number of serial images is small; gradually, patient-specific shape statistics takes more rules after a sufficient number of segmentation results on the same patient have been obtained. The proposed deformable model can adapt to the shape variability of different patients, and obtain more robust and accurate segmentation results.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages83-91
Number of pages9
Volume9
EditionPt 1
Publication statusPublished - 2006 Dec 1

ASJC Scopus subject areas

  • Medicine(all)

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  • Cite this

    Shi, Y., Qi, F., Xue, Z., Ito, K., Matsuo, H., & Shen, D. (2006). Segmenting lung fields in serial chest radiographs using both population and patient-specific shape statistics. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 9, pp. 83-91)