A Detailed Systematic Review on Retinal Image Segmentation Methods.
The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent further impacts due to diabetes and hypertension. Man...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 5; pp. 1250 - 1271 |
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| Autores principales: | , |
| Formato: | pictorial review 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=159758939&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159758939 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: 159758939 156676313 159758939 159758939 10.1007/s10278-022-00640-9 159758939 ppf: 1250 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Detailed Systematic Review on Retinal Image Segmentation Methods. aug: au: Panda, Nihar Ranjan Sahoo, Ajit Kumar affil: Department of Electronics and Communication Engineering, Silicon Institute of Technology, 751024, Bhubaneswar, Orissa, India sug: subj: Retina Pathology Conceptual Framework Neural Networks (Computer) Retinal Diseases Diagnosis Deep Learning Automation Digital Imaging Methods Tomography, Optical Coherence Ophthalmoscopy Methods Retinal Diseases Physiopathology ab: The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent further impacts due to diabetes and hypertension. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on retinal segmentation review has revealed distinct methodologies with diverse frameworks that are utilized for blood vessel separation. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, machine learning (ML) and deep learning (DL) were compared and have been reported as the best model. Moreover, different datasets were used to segment the retinal blood vessels. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, REFUGE, and CHASE. This article discloses the implementation capacity of distinct techniques implemented for each segmentation method. Finally, the finest accuracy of 98% and sensitivity of 96% were achieved for the technique of Convolution Neural Network with Ranking Support Vector Machine (CNN-rSVM). Moreover, this technique has utilized public datasets to verify efficiency. Hence, the overall review of this article has revealed a method for earlier diagnosis of diseases to deliver earlier therapy. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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