Computerized Detection of Lung Nodules on Chest Radiographs

Abstract

Most lung nodules missed by radiologists as well as Computer-Aided Diagnostic (CADe) schemes overlap ribs or clavicles in Chest Radiographs (CXRs). This chapter introduces an image-processing technique for suppressing the contrast of ribs and clavicles in CXRs by means of anatomically specific Multiple Massive-Training Artificial Neural Networks (MTANNs). For bone suppression, an MTANN is trained by use of input CXRs and the corresponding “teaching” images. The authors employed bone images obtained by the use of a dual-energy subtraction technique as the teaching images. For the effective suppression of ribs, having various spatial frequencies, the authors developed a multi-resolution MTANN consisting of multi-resolution decomposition/composition techniques and three MTANNs for three different-resolution images. After training with input CXRs and the corresponding dual-energy bone images, the multi-resolution MTANN was able to provide “bone-image-like” images which were similar to the teaching bone images. By subtracting the “bone-image-like” images from the corresponding CXRs, the authors were able to produce “soft-tissue-image-like” images in which ribs and clavicles were substantially suppressed. A single set of multi-resolution MTANNs cannot suppress all bone structures in a CXR, because the orientation, width, contrast, and density of bones differ from location to location, and the capability of a single set of multi-resolution MTANNs is limited. To address this issue, the authors developed anatomically specific multiple MTANNs which consist of eight sets of multi-resolution MTANNs that were designed to process different segments in the lung fields in a CXR. Each set of anatomically specific MTANNs was trained with only the samples in the corresponding segment in the CXR. In order to make the contrast and density between the segments consistent, the authors applied a histogram matching technique to input images. To improve the performance of their CADe scheme, the authors incorporated their MTANN bone suppression into their CADe scheme for nodules in CXRs. In their CADe scheme, 64 morphologic and gray-level-based features were extracted from each nodule candidate in both the original and the “soft-tissue-image-like images,” and a nonlinear support vector classifier was employed for the classification of the candidates. The authors used a validation test database consisting of 118 CXRs with pulmonary nodules and a publicly available database containing 126 nodules. When their technique was applied to non-training CXRs, bones in the CXRs were suppressed substantially, while the visibility of nodules and lung vessels was maintained. With the use of “soft-tissue-image-like images,” the performance of the authors’ CADe scheme was improved from a sensitivity of 76% to 84% with 5 false positives per image. Thus, the authors’ image-processing technique for bone suppression by means of anatomically specific multiple MTANNs is useful for radiologists as well as for CAD schemes in the detection of lung nodules on CXRs.