Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases.
Performing accurate and fully automated lung segmentation of high-resolution computed tomography (HRCT) images affected by dense abnormalities is a challenging problem. This paper presents a novel algorithm for automated segmentation of lungs based on modified convex hull algorithm and mathematical...
| Publicado en: | Journal of Digital Imaging Vol. 29; no. 4; pp. 507 - 520 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=116774807&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116774807 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2016 vid: 29 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 116774807 116774807 116774807 10.1007/s10278-016-9875-z 116774807 ppf: 507 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases. aug: au: Pulagam, Ammi Kande, Giri Ede, Venkata Inampudi, Ramesh affil: Vasireddy Venkatadri Institute of Technology, Nambur Guntur India sug: subj: Computed Tomography Angiography Lung Diseases Diagnosis Automation, Laboratory Diagnostic Imaging Human Algorithms ab: Performing accurate and fully automated lung segmentation of high-resolution computed tomography (HRCT) images affected by dense abnormalities is a challenging problem. This paper presents a novel algorithm for automated segmentation of lungs based on modified convex hull algorithm and mathematical morphology techniques. Sixty randomly selected lung HRCT scans with different abnormalities are used to test the proposed algorithm, and experimental results show that the proposed approach can accurately segment the lungs even in the presence of disease patterns, with some limitations in the apices and bases of lungs. The algorithm demonstrates a high segmentation accuracy (dice similarity coefficient = 98.62 and shape differentiation metrics d = 1.39 mm, and d = 2.76 mm). Therefore, the developed automated lung segmentation algorithm is a good candidate for the first stage of a computer-aided diagnosis system for diffuse lung diseases. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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