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Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high‐resolution CT of the lung
Author(s) -
Sluimer Ingrid C.,
Prokop Mathias,
Hartmann Ieneke,
van Ginneken Bram
Publication year - 2006
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2207131
Subject(s) - abnormality , feature (linguistics) , pattern recognition (psychology) , artificial intelligence , homogeneous , kappa , computer aided diagnosis , ground truth , radiology , receiver operating characteristic , mathematics , nuclear medicine , computer science , medicine , statistics , linguistics , philosophy , geometry , combinatorics , psychiatry
An automatic method for textural analysis of complete HRCT lung slices is presented. The system performs classification of regions of interest (ROIs) into one of six classes: normal, hyperlucency, fibrosis, ground glass, solid, and focal. We propose a novel method of automatically generating ROIs that contain homogeneous texture. The use of such regions rather than square regions is shown to improve performance of the automated system. Furthermore, the use of two different, previously published, feature sets is investigated. Both feature sets are shown to yield similar results. Classification performance of the complete system is characterized by ROC curves for each of the classes of abnormality and compared to a total of three expert readings by two experienced radiologists. The different types of abnormality can be automatically distinguished with areas under the ROC curve that range from 0.74 (focal) to 0.95 (solid). The kappa statistics for intraobserver agreement, interobserver agreement, and computer versus observer agreement were 0.70 , 0.53 ± 0.02 , and 0.40 ± 0.03 , respectively. The question whether or not a class of abnormality was present in a slice could be answered by the computer system with an accuracy comparable to that of radiologists.