Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening

Hidetaka Arimura, Shigehiko Katsuragawa, Kenji Suzuki, Feng Li, Junji Shiraishi, Shusuke Sone, Kunio Doi

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120 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)617-629
Number of pages13
JournalAcademic Radiology
Volume11
Issue number6
DOIs
Publication statusPublished - Jun 2004

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All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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