Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening.
OBJECTIVE
A computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening was developed.


METHODS
Our scheme is based on a difference-image technique for enhancing the lung nodules and suppressing the majority of background normal structures. The difference image for each computed tomography image was obtained by subtracting the nodule-suppressed image processed with a ring average filter from the nodule-enhanced image with a matched filter. The initial nodule candidates were identified by applying a multiple-gray level thresholding technique to the difference image, where most nodules were well enhanced. A number of false-positives were removed first in entire lung regions and second in divided lung regions by use of the two rule-based schemes on the localized image features related to morphology and gray levels. Some of the remaining false-positives were eliminated by use of a multiple massive training artificial neural network trained for reduction of various types of false-positives. This computerized scheme was applied to a confirmed cancer database of 106 low-dose computed tomography scans with 109 cancer lesions for 73 patients obtained from a lung cancer screening program in Nagano, Japan.


RESULTS
This computed-aided diagnosis scheme provided a sensitivity of 83% (91/109) for all cancers with 5.8 false-positives per scan, which included 84% (32/38) for missed cancers with 5.9 false-positives per scan.


CONCLUSIONS
This computerized scheme may be useful for assisting radiologists in detecting lung cancers on low-dose computed tomography images for lung cancer screening.
Arimura
Hidetaka
H
Department of Radiology, The University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637, USA.
Katsuragawa
Shigehiko
S
Suzuki
Kenji
K
Li
Feng
F
Shiraishi
Junji
J
Sone
Shusuke
S
Doi
Kunio
K
D005189:False Positive Reactions; D006801:Humans; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*