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 proceedingConference contribution

7 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. 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 2006 - 9th International Conference, Proceedings
PublisherSpringer Verlag
Pages83-91
Number of pages9
ISBN (Print)3540447075, 9783540447078
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - Copenhagen, Denmark
Duration: 2006 Oct 12006 Oct 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4190 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006
CountryDenmark
CityCopenhagen
Period06/10/106/10/6

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(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 2006 - 9th International Conference, Proceedings (pp. 83-91). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4190 LNCS - I). Springer Verlag. https://doi.org/10.1007/11866565_11