{"doi":"10.1109/tmi.2013.2290491","title":"Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration","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":"IEEE Transactions on Medical Imaging","year":2014,"id":8854,"datarank":0.9733807396987977,"base_score":6.489204931325317,"endowment":6.489204931325317,"self_citation_contribution":0.9733807396987977,"citation_network_contribution":0.0,"self_endowment_contribution":0.9733807396987977,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":657,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.04,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2014-02-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":75948,"name":"Stefan Jaeger","orcid":"0000-0001-6877-4318","position":1,"is_corresponding":false},{"id":75949,"name":"Kannappan Palaniappan","orcid":"0000-0003-2663-1380","position":2,"is_corresponding":false},{"id":75950,"name":"Jonathan P. Musco","orcid":null,"position":3,"is_corresponding":false},{"id":75951,"name":"Rahul K. Singh","orcid":null,"position":4,"is_corresponding":false},{"id":75952,"name":"Zhiyun Xue","orcid":"0000-0003-0644-385X","position":5,"is_corresponding":false},{"id":75953,"name":"Alexandros Karargyris","orcid":"0000-0002-1930-3410","position":6,"is_corresponding":false},{"id":75954,"name":"Sameer Antani","orcid":"0000-0002-0040-1387","position":7,"is_corresponding":false},{"id":75955,"name":"George Thoma","orcid":null,"position":8,"is_corresponding":false},{"id":75956,"name":"Clement J. McDonald","orcid":"0000-0002-0514-9539","position":9,"is_corresponding":false},{"id":75957,"name":"Rahul Kumar Singh","orcid":"0000-0002-4996-5300","position":10,"is_corresponding":false},{"id":75958,"name":"George R. Thoma","orcid":null,"position":11,"is_corresponding":false},{"id":75947,"name":"Sema Candemir","orcid":"0000-0001-8619-5619","position":0,"is_corresponding":true}],"reference_count":70,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}