Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration
National Center for Biotechnology Information · National Institutes of Health · +1 more institution
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…
Citation impact
- FWCI
- 11.95
- Percentile
- 100%
- References
- 75
Authors
10- SCSema CandemirCorresponding
National Center for Biotechnology Information, National Institutes of Health
- SJStefan Jaeger
National Center for Biotechnology Information, National Institutes of Health
- KPKannappan Palaniappan
University of Missouri
- JPJonathan P. Musco
University of Missouri
- RKRahul Kumar Singh
University of Missouri
Topics & keywords
- Artificial intelligence
- Bhattacharyya distance
- Computer science
- Robustness (evolution)
- Computer vision
- Segmentation
- Image registration
- Scale-invariant feature transform