A nonparametric-based rib suppression method for chest radiographs

Lee, Jiann-Shu; Wang, Jing-Wein; Wu, Hsing-Hsien; Yuan, Ming-Zheng

doi:10.1016/j.camwa.2012.03.084

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…The general approach is first extracting the rib pixels using an edge detection algorithm [25]. Then, the candidate rib pixels/lines are grouped into a complete rib boundary by applying a curve fitting technique [26], using a voting approach such as Hough transform [10, 27], or applying a geometric model such as parabolas [28, 29, 30, 7] or ellipses [31]. Although extracting the rib borders with an edge detection algorithm is a well-known approach, these algorithms produce spurious edges at the apex of the lung due to overlapping bone structures.…”

Section: Literature Reviewmentioning

confidence: 99%

Atlas-based rib-bone detection in chest X-rays

Candemir

Jaeger

Bağcı

et al. 2016

Computerized Medical Imaging and Graphics

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This paper investigates using rib-bone atlases for automatic detection of rib-bones in chest X-rays (CXRs). We built a system that takes patient X-ray and model atlases as input and automatically computes the posterior rib borders with high accuracy and efficiency. In addition to conventional atlas, we propose two alternative atlases: (i) automatically computed rib bone models using Computed Tomography (CT) scans, and (ii) dual energy CXRs. We test the proposed approach with each model on 25 CXRs from the Japanese Society of Radiological Technology (JSRT) dataset and another 25 CXRs from the National Library of Medicine CXR dataset. We achieve an area under the ROC curve (AUC) of about 95% for Montgomery and 91% for JSRT datasets. Using the optimal operating point of the ROC curve, we achieve a segmentation accuracy of 88.91 ± 1.8 % for Montgomery and 85.48 ± 3.3 % for JSRT datasets. Our method produces comparable results with the state-of-the-art algorithms. The performance of our method is also excellent on challenging X-rays as it successfully addressed the rib-shape variance between patients and number of visible rib-bones due to patient respiration.

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Section: Literature Reviewmentioning

confidence: 99%

Atlas-based rib-bone detection in chest X-rays

Candemir

Jaeger

Bağcı

et al. 2016

Computerized Medical Imaging and Graphics

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“…The preprocessing was carried out using Weiner Filter while feature vectors were considered for calcified part, region of interest, size, shape, and contrast etc. Lee et al [10] have presented a technique to remove the adverse effect of shadows of rib generated in chest radiographs. This study uses region of interest along with active shape model.…”

Section: The Problemmentioning

confidence: 99%

Study for Assessing the Advancement of Imaging Techniques in Chest Radiographic Images

S.K.¹,

Naveen²

2016

CAE

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With the advancement of medical image processing along with computer-aided diagnosis approach, the existing healthcare system is equipped with potential imaging devices (e.g. CT scan, MRI, PET scan etc) that assist precise diagnosis of disease. Although, there is an availability of sophisticated radiological equipments, but sometimes identification of the disease becomes the most challenging task for the physician. This paper discusses mainly about the chest radiographic images and its associated problems that still remain as an open problem in research community. Chest radiographs are normally subjected for preprocessing, feature extraction, and then followed by classification. The paper discusses about the existing research technique for the detection and classification of the disease/abnormalities in chest radiographs. Finally a research gap is explored after reviewing the existing literatures.

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“…This allows the framework to exhibit entirely in an unsupervised manner. The template extraction method is inspired from (Lee et al, 2012), where a locale sampling scheme technique is first employed to enhance the rib contrast, and then edge detection operator is used to reveal candidate edges, and finally a simple selection scheme is applied to fix the template which appears in the middle of the lung fields. The overall description of their methodology can not detail how to convert edge detected image into binary and how to select the template that appears in the middle of the lung, where both strategy in fact significantly affects the continuity of the rib structure, and in turn the quality of the final rib template extracted.…”

Section: Rib Template Extractionmentioning

confidence: 99%

Unsupervised Rib Delineation in Chest Radiographs by an Integrative Approach

Oğul¹,

Sümer

Ogul

2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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We address the problem of segmenting ribs in a chest radiography image as an intermediate step for eliminating rib shadows for an effective Computer-Aided Diagnosis System (CAD). To this end, we introduce a complete framework that takes an unprocessed x-ray image and reports the entire rib regions. The system offers a novel strategy to fit a parabola curve to all rib seeds obtained through a log Gabor filtering approach and extend the center curve by a problem-specific region growing technique to delineate the entire rib, which does not necessarily follow a general parabolic model of rib cage. The visual examinations of predicted rib delineations in a common dataset have demonstrated that the system can achieve a reasonably good performance to be used in practice.260 Ogul B., Sümer E. and Ogul H.. Unsupervised Rib Delineation in Chest Radiographs by an Integrative Approach.

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A nonparametric-based rib suppression method for chest radiographs

Cited by 16 publications

References 23 publications

Atlas-based rib-bone detection in chest X-rays

Atlas-based rib-bone detection in chest X-rays

Study for Assessing the Advancement of Imaging Techniques in Chest Radiographic Images

Unsupervised Rib Delineation in Chest Radiographs by an Integrative Approach

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