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Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images
Author(s) -
Aoyama Masahito,
Li Qiang,
Katsuragawa Shigehiko,
MacMahon Heber,
Doi Kunio
Publication year - 2002
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 - 10.1118/1.1469630
Subject(s) - nodule (geology) , linear discriminant analysis , solitary pulmonary nodule , radiology , histogram , artificial intelligence , computer aided diagnosis , radiography , pattern recognition (psychology) , lung , medicine , nuclear medicine , computer science , computed tomography , image (mathematics) , paleontology , biology
A novel automated computerized scheme has been developed to assist radiologists for their distinction between benign and malignant solitary pulmonary nodules on chest images. Our database consisted of 55 chest radiographs (33 primary lung cancers and 22 benign nodules). In this method, the location of a nodule was indicated first by a radiologist. The difference image with a nodule was produced by use of filters and then represented in a polar coordinate system. The nodule was segmented automatically by analysis of contour lines of the gray‐level distribution based on the polar‐coordinate representation. Two clinical parameters (age and sex) and 75 image features were determined from the outline, the image, and histogram analysis for inside and outside regions of the segmented nodule. Linear discriminant analysis (LDA) and knowledge about benign and malignant nodules were used to select initial feature combinations. Many combinations for subgroups of 77 features were evaluated as input to artificial neural networks (ANNs). The performance of ANNs with the selected 7 features by use of the round‐robin test showed Az = 0.872 , which was greater than Az = 0.854 obtained previously with the manual method ( P = 0.53 ) . The performance of LDA ( Az = 0.886 ) was slightly improved compared to that of ANNs ( P = 0.59 ) and was greater than that of the manual method ( Az = 0.854 ) reported previously ( P = 0.40 ) . The high level of its performance indicates the potential usefulness of this automated computerized scheme in assisting radiologists as a second opinion for distinction between benign and malignant solitary pulmonary nodules on chest images.