Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning
DC Field | Value | Language |
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dc.contributor.author | Shao, Yeqin | - |
dc.contributor.author | Gao, Yaozong | - |
dc.contributor.author | Guo, Yanrong | - |
dc.contributor.author | Shi, Yonghong | - |
dc.contributor.author | Yang, Xin | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-05T05:47:10Z | - |
dc.date.available | 2021-09-05T05:47:10Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2014-09 | - |
dc.identifier.issn | 0278-0062 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/97564 | - |
dc.description.abstract | Lung field segmentation in the posterior-anterior (PA) chest radiograph is important for pulmonary disease diagnosis and hemodialysis treatment. Due to high shape variation and boundary ambiguity, accurate lung field segmentation from chest radiograph is still a challenging task. To tackle these challenges, we propose a joint shape and appearance sparse learning method for robust and accurate lung field segmentation. The main contributions of this paper are: 1) a robust shape initialization method is designed to achieve an initial shape that is close to the lung boundary under segmentation; 2) a set of local sparse shape composition models are built based on local lung shape segments to overcome the high shape variations; 3) a set of local appearance models are similarly adopted by using sparse representation to capture the appearance characteristics in local lung boundary segments, thus effectively dealing with the lung boundary ambiguity; 4) a hierarchical deformable segmentation framework is proposed to integrate the scale-dependent shape and appearance information together for robust and accurate segmentation. Our method is evaluated on 247 PA chest radiographs in a public dataset. The experimental results show that the proposed local shape and appearance models outperform the conventional shape and appearance models. Compared with most of the state-of-the-art lung field segmentation methods under comparison, our method also shows a higher accuracy, which is comparable to the inter-observer annotation variation. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | CHEST RADIOGRAPHS | - |
dc.subject | PROSTATE | - |
dc.subject | ROBUST | - |
dc.subject | CLASSIFICATION | - |
dc.subject | LOCALIZATION | - |
dc.subject | RECOGNITION | - |
dc.subject | ALGORITHM | - |
dc.subject | FRAMEWORK | - |
dc.subject | MODELS | - |
dc.title | Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1109/TMI.2014.2305691 | - |
dc.identifier.scopusid | 2-s2.0-84906886315 | - |
dc.identifier.wosid | 000341773600001 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON MEDICAL IMAGING, v.33, no.9, pp.1761 - 1780 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON MEDICAL IMAGING | - |
dc.citation.title | IEEE TRANSACTIONS ON MEDICAL IMAGING | - |
dc.citation.volume | 33 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 1761 | - |
dc.citation.endPage | 1780 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | CHEST RADIOGRAPHS | - |
dc.subject.keywordPlus | PROSTATE | - |
dc.subject.keywordPlus | ROBUST | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | LOCALIZATION | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordAuthor | Active shape model | - |
dc.subject.keywordAuthor | chest radiograph | - |
dc.subject.keywordAuthor | deformable segmentation | - |
dc.subject.keywordAuthor | local appearance model | - |
dc.subject.keywordAuthor | local sparse shape composition | - |
dc.subject.keywordAuthor | sparse learning | - |
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