Literature DB >> 15288037

Quantitative evaluation of a pulmonary contour segmentation algorithm in X-ray computed tomography images.

Beatriz Sousa Santos1, Carlos Ferreira, José Silvestre Silva, Augusto Silva, Luísa Teixeira.   

Abstract

RATIONALE AND
OBJECTIVES: Pulmonary contour extraction from thoracic x-ray computed tomography images is a mandatory preprocessing step in many automated or semiautomated analysis tasks. This study was conducted to quantitatively assess the performance of a method for pulmonary contour extraction and region identification.
MATERIALS AND METHODS: The automatically extracted contours were statistically compared with manually drawn pulmonary contours detected by six radiologists on a set of 30 images. Exploratory data analysis, nonparametric statistical tests, and multivariate analysis were used, on the data obtained using several figures of merit, to perform a study of the interobserver variability among the six radiologists and the contour extraction method. The intraobserver variability of two human observers was also studied.
RESULTS: In addition to a strong consistency among all of the quality indexes used, a wider interobserver variability was found among the radiologists than the variability of the contour extraction method when compared with each radiologist. The extraction method exhibits a similar behavior (as a pulmonary contour detector), to the six radiologists, for the used image set.
CONCLUSION: As an overall result of the application of this evaluation methodology, the consistency and accuracy of the contour extraction method was confirmed to be adequate for most of the quantitative requirements of radiologists. This evaluation methodology could be applied to other scenarios.

Entities:  

Mesh:

Year:  2004        PMID: 15288037     DOI: 10.1016/j.acra.2004.05.004

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  5 in total

1.  Adaptive border marching algorithm: automatic lung segmentation on chest CT images.

Authors:  Jiantao Pu; Justus Roos; Chin A Yi; Sandy Napel; Geoffrey D Rubin; David S Paik
Journal:  Comput Med Imaging Graph       Date:  2008-06-02       Impact factor: 4.790

2.  A Segmentation Framework of Pulmonary Nodules in Lung CT Images.

Authors:  Sudipta Mukhopadhyay
Journal:  J Digit Imaging       Date:  2016-02       Impact factor: 4.056

3.  Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients.

Authors:  Luca Saba; Joel C M Than; Norliza M Noor; Omar M Rijal; Rosminah M Kassim; Ashari Yunus; Chue R Ng; Jasjit S Suri
Journal:  J Med Syst       Date:  2016-04-25       Impact factor: 4.460

4.  Fast volumetric registration method for tumor follow-up in pulmonary CT exams.

Authors:  José Silvestre Silva; João Cancela; Luísa Teixeira
Journal:  J Appl Clin Med Phys       Date:  2011-02-02       Impact factor: 2.102

5.  Deformable registration using edge-preserving scale space for adaptive image-guided radiation therapy.

Authors:  Dengwang Li; Hongjun Wang; Yong Yin; Xiuying Wang
Journal:  J Appl Clin Med Phys       Date:  2011-11-15       Impact factor: 2.102

  5 in total

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