Retinal Vessel Detection and Measurement for Computer-aided Medical Diagnosis.
Since blood vessel detection and characteristic measurement for ocular retinal images is a fundamental problem in computer-aided medical diagnosis, automated algorithms/systems for vessel detection and measurement are always demanded. To support computer-aided diagnosis, an integrated approach/solut...
| Publicado en: | Journal of Digital Imaging Vol. 27; no. 1; pp. 120 - 133 |
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| Autores principales: | , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2014
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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=104013602&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104013602 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2014 vid: 27 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104013602 94061939 10.1007/s10278-013-9639-y NLM24081671 PMC3903970 104013602 ppf: 120 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Retinal Vessel Detection and Measurement for Computer-aided Medical Diagnosis. aug: au: Li, Xiaokun Wee, William affil: TASC, Inc, 475 School Street SW Washington 20024 USA sug: subj: Diagnosis, Computer Assisted Retina Radiography Retinal Diseases Diagnosis Radiographic Image Enhancement Methods Radiographic Image Interpretation, Computer-Assisted Methods Algorithms Artificial Intelligence False Negative Results False Positive Results Evaluation Research Descriptive Statistics Comparative Studies Human ab: Since blood vessel detection and characteristic measurement for ocular retinal images is a fundamental problem in computer-aided medical diagnosis, automated algorithms/systems for vessel detection and measurement are always demanded. To support computer-aided diagnosis, an integrated approach/solution for vessel detection and diameter measurement is presented and validated. In the proposed approach, a Dempster-Shafer (D-S)-based edge detector is developed to obtain initial vessel edge information and an accurate vascular map for a retinal image. Then, the appropriate path and the centerline of a vessel of interest are identified automatically through graph search. Once the vessel path has been identified, the diameter of the vessel will be measured accordingly by the algorithm in real time. To achieve more accurate edge detection and diameter measurement, mixed Gaussian-matched filters are designed to refine the initial detection and measures. Other important medical indices of retinal vessels can also be calculated accordingly based on detection and measurement results. The efficiency of the proposed algorithm was validated by the retinal images obtained from different public databases. Experimental results show that the vessel detection rate of the algorithm is 100 % for large vessels and 89.9 % for small vessels, and the error rate on vessel diameter measurement is less than 5 %, which are all well within the acceptable range of deviation among the human graders. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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