A Novel Microaneurysms Detection Method Based on Local Applying of Markov Random Field.
Diabetic Retinopathy (DR) is one of the most common complications of long-term diabetes. It is a progressive disease and by damaging retina, it finally results in blindness of patients. Since Microaneurysms (MAs) appear as a first sign of DR in retina, early detection of this lesion is an essential...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 3; pp. 1 - 10 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2016
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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=115925282&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925282 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Mar2016 vid: 40 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925282 115925282 115925282 10.1007/s10916-016-0434-4 115925282 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A Novel Microaneurysms Detection Method Based on Local Applying of Markov Random Field. aug: au: Ganjee, Razieh Azmi, Reza Ebrahimi Moghadam, Mohsen affil: Faculty of Computer Science Engineering, Shahid Beheshti University: G.C, Tehran Iran sug: subj: Diabetic Retinopathy Symptoms Microaneurysm Diagnosis Early Diagnosis Methods Models, Statistical Utilization Human Confidence Intervals Sensitivity and Specificity Descriptive Statistics Algorithms ROC Curve Experimental Studies ab: Diabetic Retinopathy (DR) is one of the most common complications of long-term diabetes. It is a progressive disease and by damaging retina, it finally results in blindness of patients. Since Microaneurysms (MAs) appear as a first sign of DR in retina, early detection of this lesion is an essential step in automatic detection of DR. In this paper, a new MAs detection method is presented. The proposed approach consists of two main steps. In the first step, the MA candidates are detected based on local applying of Markov random field model (MRF). In the second step, these candidate regions are categorized to identify the correct MAs using 23 features based on shape, intensity and Gaussian distribution of MAs intensity. The proposed method is evaluated on DIARETDB1 which is a standard and publicly available database in this field. Evaluation of the proposed method on this database resulted in the average sensitivity of 0.82 for a confidence level of 75 as a ground truth. The results show that our method is able to detect the low contrast MAs with the background while its performance is still comparable to other state of the art approaches. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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