Hybrid detection of lung nodules on CT scan images.
OBJECTIVE
The diversity of lung nodules poses difficulty for the current computer-aided diagnostic (CAD) schemes for lung nodule detection on computed tomography (CT) scan images, especially in large-scale CT screening studies. We proposed a novel CAD scheme based on a hybrid method to address the challenges of detection in diverse lung nodules.


METHODS
The hybrid method proposed in this paper integrates several existing and widely used algorithms in the field of nodule detection, including morphological operation, dot-enhancement based on Hessian matrix, fuzzy connectedness segmentation, local density maximum algorithm, geodesic distance map, and regression tree classification. All of the adopted algorithms were organized into tree structures with multi-nodes. Each node in the tree structure aimed to deal with one type of lung nodule.


RESULTS
The method has been evaluated on 294 CT scans from the Lung Image Database Consortium (LIDC) dataset. The CT scans were randomly divided into two independent subsets: a training set (196 scans) and a test set (98 scans). In total, the 294 CT scans contained 631 lung nodules, which were annotated by at least two radiologists participating in the LIDC project. The sensitivity and false positive per scan for the training set were 87% and 2.61%. The sensitivity and false positive per scan for the testing set were 85.2% and 3.13%.


CONCLUSIONS
The proposed hybrid method yielded high performance on the evaluation dataset and exhibits advantages over existing CAD schemes. We believe that the present method would be useful for a wide variety of CT imaging protocols used in both routine diagnosis and screening studies.
Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032.
Lin
L
Lu
Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032.
Yongqiang
Y
Tan
Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032.
Lawrence H
LH
Schwartz
Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032.
Binsheng
B
Zhao
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D011877:Radionuclide Imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*