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Proceedings Paper

Comparison of demons deformable registration-based methods for texture analysis of serial thoracic CT scans
Author(s): Alexandra R. Cunliffe; Hania A. Al-Hallaq; Xianhan M. Fei; Rachel E. Tuohy; Samuel G. Armato
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Paper Abstract

To determine how 19 image texture features may be altered by three image registration methods, “normal” baseline and follow-up computed tomography (CT) scans from 27 patients were analyzed. Nineteen texture feature values were calculated in over 1,000 32x32-pixel regions of interest (ROIs) randomly placed in each baseline scan. All three methods used demons registration to map baseline scan ROIs to anatomically matched locations in the corresponding transformed follow-up scan. For the first method, the follow-up scan transformation was subsampled to achieve a voxel size identical to that of the baseline scan. For the second method, the follow-up scan was transformed through affine registration to achieve global alignment with the baseline scan. For the third method, the follow-up scan was directly deformed to the baseline scan using demons deformable registration. Feature values in matched ROIs were compared using Bland- Altman 95% limits of agreement. For each feature, the range spanned by the 95% limits was normalized to the mean feature value to obtain the normalized range of agreement, nRoA. Wilcoxon signed-rank tests were used to compare nRoA values across features for the three methods. Significance for individual tests was adjusted using the Bonferroni method. nRoA was significantly smaller for affine-registered scans than for the resampled scans (p=0.003), indicating lower feature value variability between baseline and follow-up scan ROIs using this method. For both of these methods, however, nRoA was significantly higher than when feature values were calculated directly on demons-deformed followup scans (p<0.001). Across features and methods, nRoA values remained below 26%.

Paper Details

Date Published: 28 February 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700D (28 February 2013); doi: 10.1117/12.2007046
Show Author Affiliations
Alexandra R. Cunliffe, The Univ. of Chicago Medical Ctr. (United States)
Hania A. Al-Hallaq, The Univ. of Chicago Medical Ctr. (United States)
Xianhan M. Fei, The Univ. of Chicago Medical Ctr. (United States)
Rachel E. Tuohy, The Univ. of Chicago Medical Ctr. (United States)
Samuel G. Armato, The Univ. of Chicago Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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