Literature DB >> 23880041

Suppression of translucent elongated structures: applications in chest radiography.

Laurens Hogeweg, Clara I Sanchez, Bram van Ginneken.   

Abstract

Projection images, such as those routinely acquired in radiological practice, are difficult to analyze because multiple 3-D structures superimpose at a single point in the 2-D image. Removal of particular superimposed structures may improve interpretation of these images, both by humans and by computers. This work therefore presents a general method to isolate and suppress structures in 2-D projection images. The focus is on elongated structures, which allows an intensity model of a structure of interest to be extracted using local information only. The model is created from profiles sampled perpendicular to the structure. Profiles containing other structures are detected and removed to reduce the influence on the model. Subspace filtering, using blind source separation techniques, is applied to separate the structure to be suppressed from other structures. By subtracting the modeled structure from the original image a structure suppressed image is created. The method is evaluated in four experiments. In the first experiment ribs are suppressed in 20 artificial radiographs simulated from 3-D lung computed tomography (CT) images. The proposed method with blind source separation and outlier detection shows superior suppression of ribs in simulated radiographs, compared to a simplified approach without these techniques. Additionally, the ability of three observers to discriminate between patches containing ribs and containing no ribs, as measured by the area under the receiver operating characteristic curve (AUC), reduced from 0.99-1.00 on original images to 0.75-0.84 on suppressed images. In the second experiment clavicles are suppressed in 253 chest radiographs. The effect of suppression on clavicle visibility is evaluated using the clavicle contrast and border response, showing a reduction of 78% and 34%, respectively. In the third experiment nodules extracted from CT were simulated close to the clavicles in 100 chest radiographs. It was found that after suppression contrast of the nodules was higher than of the clavicles (1.35 and 0.55, respectively) than on original images (1.83 and 2.46, respectively). In the fourth experiment catheters were suppressed in chest radiographs. The ability of three observers to discriminate between patches originating from 36 images with and 21 images without catheters, as measured by the AUC, reduced from 0.98-0.99 on original images to 0.64-0.74 on suppressed images. We conclude that the presented method can markedly reduce the visibility of elongated structures in chest radiographs and shows potential to enhance diagnosis.

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Year:  2013        PMID: 23880041     DOI: 10.1109/TMI.2013.2274212

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  5 in total

1.  A novel bone suppression method that improves lung nodule detection : Suppressing dedicated bone shadows in radiographs while preserving the remaining signal.

Authors:  Jens von Berg; Stewart Young; Heike Carolus; Robin Wolz; Axel Saalbach; Alberto Hidalgo; Ana Giménez; Tomás Franquet
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-09-04       Impact factor: 2.924

2.  Deep learning-based bone suppression in chest radiographs using CT-derived features: a feasibility study.

Authors:  Ge Ren; Haonan Xiao; Sai-Kit Lam; Dongrong Yang; Tian Li; Xinzhi Teng; Jing Qin; Jing Cai
Journal:  Quant Imaging Med Surg       Date:  2021-12

Review 3.  Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

Authors:  Bram van Ginneken
Journal:  Radiol Phys Technol       Date:  2017-02-16

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

Authors:  Yunbi Liu; Wei Yang; Guangnan She; Liming Zhong; Zhaoqiang Yun; Yang Chen; Ni Zhang; Liwei Hao; Zhentai Lu; Qianjin Feng; Wufan Chen
Journal:  Appl Bionics Biomech       Date:  2019-06-24       Impact factor: 1.781

5.  Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings.

Authors:  Sivaramakrishnan Rajaraman; Ghada Zamzmi; Les Folio; Philip Alderson; Sameer Antani
Journal:  Diagnostics (Basel)       Date:  2021-05-07
  5 in total

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