Image feature analysis of false-positive diagnoses produced by automated detection of lung nodules.
OBJECTIVE
To reduce the number of false-negative diagnoses by radiologists, the authors are developing a computer-aided diagnosis scheme for detection of lung nodules in digital chest images. In this study, the authors attempted to reduce the number of false-positive diagnoses obtained with a previous computer scheme by incorporating additional knowledge from experienced chest radiologists into the computer scheme.


METHODS
The authors applied their previous computer scheme, using less-strict criteria, to 60 clinical chest radiographs; this yielded 735 candidate nodules (23 true nodules and 712 false-positive diagnoses). These candidates were analyzed using region-growing, trend-correction, and edge-gradient techniques to determine measures by which to quantify image features of candidate nodules.


RESULTS
The 712 false-positive diagnoses represented various anatomic structures that were located throughout the chest image. From this analysis, we were able to decrease the number of false-positive errors from an average of 12 to approximately 5 per image without eliminating any true nodules.


CONCLUSIONS
Our results show that incorporating knowledge from experienced chest radiologists into the computer algorithm will play an important role in the development of computerized schemes for the detection of pulmonary nodules.
Matsumoto
T
T
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637.
Yoshimura
H
H
Doi
K
K
Giger
M L
ML
Kano
A
A
MacMahon
H
H
Abe
K
K
Montner
S M
SM
D003951:Diagnostic Errors; D005189:False Positive Reactions; D006801:Humans; D008168:Lung / Q000098:blood supply / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D012272:Ribs / Q000000981:diagnostic imaging