Usual interstitial pneumonia. Quantitative assessment of high-resolution computed tomography findings by computer-assisted texture-based image analysis.
OBJECTIVE
The authors developed a texture-based pattern recognition and segmentation tool for the quantitation of high-resolution computed tomography (HRCT) findings in usual interstitial pneumonia (UIP).


METHODS
In HRCT images of five patients with UIP and five patients without UIP, 1022 regions of interest (ROIs) of 5 x 5 pixels were classified by the examiner to be normal, emphysematous, ground-glass lesion, intralobular fibrosis, vessel, or bronchus section. The classes and the texture parameters calculated in the ROIs were the basis for the decision rule, using a multivariate discrimination analysis. The classification was compared with the examiner's diagnosis in 1889 new randomly selected ROIs.


RESULTS
Depending on the structure, the sensitivity (the probability that a structure would be recognized correctly) was 68.7% to 80.7%. If the system classified a structure as normal, ground glass or fibrotic region, this was correct in 77.3% to 88.1%. However, the system's diagnosis of a bronchus section was correct in only 16.2%. The overall accuracy was 70.7%.


CONCLUSIONS
Texture-based segmentation may be a valuable tool to aid the quantitative assessment of parenchymal disease in HRCT images.
Delorme
S
S
German Cancer Research Center, Department of Radiological Diagnostic and Therapy, Heidelberg, Germany. s.delorme@dkfz-heidelberg.de
Keller-Reichenbecher
M A
MA
Zuna
I
I
Schlegel
W
W
Van Kaick
G
G
D000328:Adult; D000368:Aged; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D017563:Lung Diseases, Interstitial / Q000145:classification / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011446:Prospective Studies; D011897:Random Allocation; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*