Paper Info
Title
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.
Year
DOI
Venue
2014
10.1109/TMI.2013.2284016
IEEE Trans. Med. Imaging
Keywords
Field
DocType
computer-aided detection,anatomically specific multiple massive-training ann,anatomically specific multiple massive-training artificial neural networks,diagnostic radiography,total variation minimization smoothing,virtual dual-energy,chest radiography,output segmental images,image segmentation,lung,histogram-matching-based consistency improvement method,bone,image denoising,lung nodules,chest radiographs,ribs,bone separation,anatomic segments,noise reduction,soft-tissue image,bone image,radiologists,clavicles,bony structures,minimisation,bone-soft-tissue separation,neural nets,medical image processing,lung wall,pixel-based machine learning
Rib cage,Image segmentation,Smoothing,Total variation minimization,Radiography,Image denoising,Radiology,Soft tissue,Medicine
Journal
Volume
Issue
ISSN
33
2
1558-254X
Citations 
PageRank 
References 
7
0.47
8
Authors
2
Name
Order
Citations
PageRank
S. Chen112210.19
Kenji Suzuki212726.67