Abstract：
:The National Library of Medicine (NLM) is developing a digital chest X-ray (CXR) screening system for deployment in resource constrained communities and developing countries worldwide with a focus on early detection of tuberculosis. A critical component in the computer-aided diagnosis of digital CXRs is the automatic detection of the lung regions. In this paper, we present a nonrigid registration-driven robust lung segmentation method using image retrieval-based patient specific adaptive lung models that detects lung boundaries, surpassing state-of-the-art performance. The method consists of three main stages: 1) a content-based image retrieval approach for identifying training images (with masks) most similar to the patient CXR using a partial Radon transform and Bhattacharyya shape similarity measure, 2) creating the initial patient-specific anatomical model of lung shape using SIFT-flow for deformable registration of training masks to the patient CXR, and 3) extracting refined lung boundaries using a graph cuts optimization approach with a customized energy function. Our average accuracy of 95.4% on the public JSRT database is the highest among published results. A similar degree of accuracy of 94.1% and 91.7% on two new CXR datasets from Montgomery County, MD, USA, and India, respectively, demonstrates the robustness of our lung segmentation approach.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Candemir S,Jaeger S,Palaniappan K,Musco JP,Singh RK,Zhiyun Xue,Karargyris A,Antani S,Thoma G,McDonald CJdoi
10.1109/TMI.2013.2290491subject
Has Abstractpub_date
2014-02-01 00:00:00pages
577-90issue
2eissn
0278-0062issn
1558-254Xjournal_volume
33pub_type
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