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Applied Bionics and Biomechanics
Volume 2019, Article ID 9806464, 17 pages
https://doi.org/10.1155/2019/9806464
Research Article

Soft Tissue/Bone Decomposition of Conventional Chest Radiographs Using Nonparametric Image Priors

1School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
2Laboratory of Image Science and Technology, Southeast University, Nanjing 211189, China
3Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 210096, China
4Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

Correspondence should be addressed to Wei Yang; moc.liamg@mggnayiew

Received 31 December 2018; Revised 1 March 2019; Accepted 12 May 2019; Published 24 June 2019

Guest Editor: Yuan-Chiao Lu

Copyright © 2019 Yunbi Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background and Objective. When radiologists diagnose lung diseases in chest radiography, they can miss some lung nodules overlapped with ribs or clavicles. Dual-energy subtraction (DES) imaging performs well because it can produce soft tissue images, in which the bone components in chest radiography were almost suppressed but the visibility of nodules and lung vessels was still maintained. However, most routinely available X-ray machines do not possess the DES function. Thus, we presented a data-driven decomposition model to perform virtual DES function for decomposing a single conventional chest radiograph into soft tissue and bone images. Methods. For a given chest radiograph, similar chest radiographs with corresponding DES soft tissue and bone images are selected from the training database as exemplars for decomposition. The corresponding fields between the observed chest radiograph and the exemplars are solved by a hierarchically dense matching algorithm. Then, nonparametric priors of soft tissue and bone components are constructed by sampling image patches from the selected soft tissue and bone images according to the corresponding fields. Finally, these nonparametric priors are integrated into our decomposition model, the energy function of which is efficiently optimized by an iteratively reweighted least-squares scheme (IRLS). Results. The decomposition method is evaluated on a data set of posterior-anterior DES radiography (503 cases), as well as on the JSRT data set. The proposed method can produce soft tissue and bone images similar to those produced by the actual DES system. Conclusions. The proposed method can markedly reduce the visibility of bony structures in chest radiographs and shows potential to enhance diagnosis.