TY - JOUR
T1 - Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening
AU - Arimura, Hidetaka
AU - Katsuragawa, Shigehiko
AU - Suzuki, Kenji
AU - Li, Feng
AU - Shiraishi, Junji
AU - Sone, Shusuke
AU - Doi, Kunio
N1 - Funding Information:
Supported by US Public Health Service grant nos. CA 62625 and CA 98119.
PY - 2004/6
Y1 - 2004/6
N2 - Rationale and Objectives. A computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening was developed. Materials and 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. Conclusion. This computerized scheme may be useful for assisting radiologists in detecting lung cancers on low-dose computed tomography images for lung cancer screening.
AB - Rationale and Objectives. A computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening was developed. Materials and 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. Conclusion. This computerized scheme may be useful for assisting radiologists in detecting lung cancers on low-dose computed tomography images for lung cancer screening.
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U2 - 10.1016/j.acra.2004.02.009
DO - 10.1016/j.acra.2004.02.009
M3 - Article
C2 - 15172364
AN - SCOPUS:2542474244
VL - 11
SP - 617
EP - 629
JO - Academic Radiology
JF - Academic Radiology
SN - 1076-6332
IS - 6
ER -