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Proceedings Paper

A computer-aided differential diagnosis between UIP and NSIP using automated assessment of the extent and distribution of regional disease patterns at HRCT: comparison with the radiologist's decision
Author(s): Namkug Kim; Joon Beom Seo; Sang Ok Park; Youngjoo Lee; Jeongjin Lee
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Paper Abstract

To evaluate the accuracy of computer aided differential diagnosis (CADD) between usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP) at HRCT in comparison with that of a radiologist's decision. A computerized classification for six local disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EM; and consolidation, CON) using texture/shape analyses and a SVM classifier at HRCT was used for pixel-by-pixel labeling on the whole lung area. The mode filter was applied on the results to reduce noise. Area fraction (AF) of each pattern, directional probabilistic density function (pdf) (dPDF: mean, SD, skewness of pdf /3 directions: superior-inferior, anterior-posterior, central-peripheral), regional cluster distribution pattern (RCDP: number, mean, SD of clusters, mean, SD of centroid of clusters) were automatically evaluated. Spatially normalized left and right lungs were evaluated separately. Disease division index (DDI) on every combination of AFs and asymmetric index (AI) between left and right lung ((left-right)/left) were also evaluated. To assess the accuracy of the system, fifty-four HRCT data sets in patients with pathologically diagnosed UIP (n=26) and NSIP (n=28) were used. For a classification procedure, a CADD-SVM classifier with internal parameter optimization, and sequential forward floating feature selection (SFFS) were employed. The accuracy was assessed by a 5-folding cross validation with 20- times repetition. For comparison, two thoracic radiologists reviewed the whole HRCT images without clinical information and diagnose each case either as UIP or NSIP. The accuracies of radiologists' decision were 0.75 and 0.87, respectively. The accuracies of the CADD system using the features of AF, dPDF, AI of dPDF, RDP, AI of RDP, DDI were 0.70, 0.79, 0.77, 0.80, 0.78, 0.81, respectively. The accuracy of optimized CADD using all features after SFFS was 0.91. We developed the CADD system to differentiate between UIP and NSIP using automated assessment of the extent and distribution of regional disease patterns at HRCT.

Paper Details

Date Published: 27 February 2009
PDF: 7 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601E (27 February 2009); doi: 10.1117/12.811359
Show Author Affiliations
Namkug Kim, Univ. of Ulsan College of Medicine (Korea, Republic of)
Seoul National Univ. College of Engineering (Korea, Republic of)
Joon Beom Seo, Univ. of Ulsan College of Medicine (Korea, Republic of)
Sang Ok Park, Univ. of Ulsan College of Medicine (Korea, Republic of)
Youngjoo Lee, Seoul National Univ. College of Engineering (Korea, Republic of)
Jeongjin Lee, Univ. of Ulsan College of Medicine (Korea, Republic of)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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