Separation of Bones From Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined With Total Variation Minimization Smoothing

Abstract

Most lung nodules that are missed by radiologists as well as computer-aided detection (CADe) schemes overlap with ribs or clavicles in chest radiographs (CXRs). The purpose of this study was to separate bony structures such as ribs and clavicles from soft tissue in CXRs. To achieve this, we developed anatomically specific multiple massive-training artificial neural networks (MTANNs) combined with total variation (TV) minimization smoothing and a histogram-matching-based consistency improvement method. The anatomically specific multiple MTANNs were designed to separate bones from soft tissue in different anatomic segments of the lungs. Each of the MTANNs was trained with the corresponding anatomic segment in the teaching bone images. The output segmental images from the multiple MTANNs were merged to produce an entire bone image. TV minimization smoothing was applied to the bone image for reduction of noise while preserving edges. This bone image was then subtracted from the original CXR to produce a soft-tissue image where bones were separated out. This new method was compared with conventional MTANNs with a database of 110 CXRs with nodules. Our new anatomically specific MTANNs separated rib edges, ribs close to the lung wall, and the clavicles from soft tissue in CXRs to a substantially higher level than did the conventional MTANNs, while the conspicuity of lung nodules and vessels was maintained. Thus, our technique for bone-soft-tissue separation by means of our new MTANNs would be potentially useful for radiologists as well as CADe schemes in detection of lung nodules on CXRs.

Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

“…12(a)]. This shortcoming could potentially be alleviated by introducing a bony structure suppression technique, 26,[35][36][37][38][39] prior to the execution of the cavity segmentation method. When cavities are present in the diseased region, it is very hard to define and identify the correct cavity contour [ Fig.…”

Computerized Detection of Lesions in Diagnostic Images

“…The class of MTANNs was used for classification, such as FP reduction in CAD schemes for the detection of lung nodules in CXR [134] and thoracic CT [3,65,121], distinction between benign and malignant lung nodules in CT [131], and FP reduction in a CAD scheme for polyp detection in CT colonography [132,[139][140][141]159]. The MTANNs were also applied to pattern enhancement and suppression such as separation of bones from soft tissue in CXR [19,89,120], and enhancement of lung nodules in CT [111]. There are other PML approaches in the literature.…”

“…The complete high-resolution image is expected to be similar to the teaching bone image; therefore, the multiresolution MTANN would provide a "bone-image-like" image in which ribs and clavicles are separated from soft tissues. Chen and Suzuki [19] improved the performance of the MTANN "virtual" dual-energy chest radiography by means of anatomically specific multiple MTANNs. Figure 7.11 illustrates suppression of bones from soft tissue in CXR by using the MTANNs [19].…”

“…Chen and Suzuki [19] improved the performance of the MTANN "virtual" dual-energy chest radiography by means of anatomically specific multiple MTANNs. Figure 7.11 illustrates suppression of bones from soft tissue in CXR by using the MTANNs [19].…”

Robust pulmonary segmentation for chest radiography, combining enhancement, adaptive morphology and innovative active contours

“…Radiological examination is the oldest and most used medical imaging technique to assist diagnosis and medical treatment (Chen and Suzuki, 2014). X-ray based image modalities discriminate tissues by representing the intensity of the attenuation coefficient of each tissue (Chaudhary and Patil, 2013).…”