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Computer‐aided diagnosis in high resolution CT of the lungs
Author(s) -
Sluimer Ingrid C.,
van Waes Paul F.,
Viergever Max A.,
van Ginneken Bram
Publication year - 2003
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 - 10.1118/1.1624771
Subject(s) - cad , computer aided diagnosis , receiver operating characteristic , computed tomography , computer science , artificial intelligence , feature (linguistics) , tomography , radiology , image resolution , medical imaging , computer aided , pattern recognition (psychology) , medicine , linguistics , philosophy , machine learning , engineering drawing , engineering , programming language
A computer‐aided diagnosis (CAD) system is presented to automatically distinguish normal from abnormal tissue in high‐resolution CT chest scans acquired during daily clinical practice. From high‐resolution computed tomography scans of 116 patients, 657 regions of interest are extracted that are to be classified as displaying either normal or abnormal lung tissue. A principled texture analysis approach is used, extracting features to describe local image structure by means of a multi‐scale filter bank. The use of various classifiers and feature subsets is compared and results are evaluated with ROC analysis. Performance of the system is shown to approach that of two expert radiologists in diagnosing the local regions of interest, with an area under the ROC curve of 0.862 for the CAD scheme versus 0.877 and 0.893 for the radiologists.