Purpose: Our previous study indicated that multiprojection chest radiography could significantly improve radiologists’; performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme. Methods: Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification of initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. Themore integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave-one-subject-out testing method was used for evaluation of the performance levels of the two CAD schemes. Results: At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively.

The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules. Conclusions: This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection. less

1. Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705 and College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004 (China)
2. (United States)
3. (Japan)

Publication Date: 2012-04-15 OSTI Identifier: 22098814 Resource Type: Journal Article Resource Relation: Journal Name: Medical Physics; Journal Volume: 39; Journal Issue: 4; Other Information: (c) 2012 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA) Country of Publication: United States Language: English Subject: 62 RADIOLOGY AND NUCLEAR MEDICINE ; CHEST ; COMPUTERIZED TOMOGRAPHY ; CORRELATIONS ; DETECTION ; DYNAMIC PROGRAMMING ; EVALUATION ; EXTRACTION ; IMAGE PROCESSING ; LEAVES ; LUNGS ; NEOPLASMS ; NOISE ; SENSITIVITY ; SIMULATION

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