Atlas-based rib-bone detection in chest X-rays. (July 2016)

• Record Type: Journal Article
• Authors: Candemir, Sema; Jaeger, Stefan; Antani, Sameer; Bagci, Ulas; Folio, Les R.; Xu, Ziyue; Thoma, George
• Is Part Of: Computerized medical imaging and graphics. Volume 51(2016)
• Page Start: 32
• Page End: 39
• Publication Date: 2016-07
• Subjects: Chest X-rays -- Rib bone extraction; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2016.04.002
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: Automated system which detects the rib-bones in patient chest X-rays. In addition to traditional atlas, two alternative atlases usage. Successfully rib-bone localization for patient X-rays. Comparable results with the state-of-the-art algorithm. Good results on challenging X-rays: successfully addressing the rib-shape variance between patients, and number of visible rib bones due to inhale condition of the patient. Abstract: 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.