
Proceedings Paper
Automatic segmentation of lung parenchyma based on curvature of ribs using HRCT images in scleroderma studiesFormat | Member Price | Non-Member Price |
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Paper Abstract
Segmentation of lungs in the setting of scleroderma is a major challenge in medical image analysis.
Threshold based techniques tend to leave out lung regions that have increased attenuation, for example in
the presence of interstitial lung disease or in noisy low dose CT scans. The purpose of this work is to
perform segmentation of the lungs using a technique that selects an optimal threshold for a given
scleroderma patient by comparing the curvature of the lung boundary to that of the ribs. Our approach is
based on adaptive thresholding and it tries to exploit the fact that the curvature of the ribs and the curvature
of the lung boundary are closely matched. At first, the ribs are segmented and a polynomial is used to
represent the ribs' curvature. A threshold value to segment the lungs is selected iteratively such that the
deviation of the lung boundary from the polynomial is minimized. A Naive Bayes classifier is used to build
the model for selection of the best fitting lung boundary. The performance of the new technique was
compared against a standard approach using a simple fixed threshold of -400HU followed by regiongrowing.
The two techniques were evaluated against manual reference segmentations using a volumetric
overlap fraction (VOF) and the adaptive threshold technique was found to be significantly better than the
fixed threshold technique.
Paper Details
Date Published: 1 April 2008
PDF: 10 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152K (1 April 2008); doi: 10.1117/12.769503
Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)
PDF: 10 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152K (1 April 2008); doi: 10.1117/12.769503
Show Author Affiliations
M. N. Prasad, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
M. S. Brown, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
S. Ahmad, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
F. Abtin, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
J. Allen, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
M. S. Brown, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
S. Ahmad, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
F. Abtin, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
J. Allen, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
I. da Costa, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
H. J. Kim, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
M. F. McNitt-Gray, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
J. G. Goldin, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
H. J. Kim, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
M. F. McNitt-Gray, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
J. G. Goldin, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)
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