Classification of Glaucoma Stages Using Image Empirical Mode Decomposition from Fundus Images.
One of the most prevalent causes of visual loss and blindness is glaucoma. Conventionally, instrument-based tools are employed for glaucoma screening. However, they are inefficient, time-consuming, and manual. Hence, computerized methodologies are needed for fast and accurate diagnosis of glaucoma....
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 5; pp. 1283 - 1293 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2022
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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=159758944&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159758944 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2022 vid: 35 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159758944 159758944 159758944 10.1007/s10278-022-00648-1 159758944 ppf: 1283 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of Glaucoma Stages Using Image Empirical Mode Decomposition from Fundus Images. aug: au: Parashar, Deepak Agrawal, Dheraj Kumar affil: Department of Electronics and Communication Engineering, IES College of Technology, 462044, Bhopal, MP, India sug: subj: Glaucoma Classification Glaucoma Diagnosis Diagnosis, Computer Assisted Methods Diagnosis, Eye Methods Ophthalmoscopy Methods Predictive Value of Tests Evaluation Human Photography Factor Analysis Analysis of Variance Validity Sensitivity and Specificity Image Processing, Computer Assisted ab: One of the most prevalent causes of visual loss and blindness is glaucoma. Conventionally, instrument-based tools are employed for glaucoma screening. However, they are inefficient, time-consuming, and manual. Hence, computerized methodologies are needed for fast and accurate diagnosis of glaucoma. Therefore, we proposed a Computer-Aided Diagnosis (CAD) method for the classification of glaucoma stages using Image Empirical Mode decomposition (IEMD). In this study, IEMD is applied to decompose the preprocessed fundus photographs into different Intrinsic Mode Functions (IMFs) to capture the pixel variations. Then, the significant texture-based descriptors have been computed from the IMFs. A dimensionality reduction approach called Principal Component Analysis (PCA) has been employed to pick the robust descriptors from the retrieved feature set. We used the Analysis of Variance (ANOVA) test for feature ranking. Finally, the LS-SVM classifier has been employed to classify glaucoma stages. The proposed CAD system achieved a classification accuracy of 94.45% for the binary classification on the RIM-ONE r12 database. Our approach demonstrated better glaucoma classification performance than the existing automated systems. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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