Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique.
Image segmentation is a crucial task in computer vision and image processing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compressio...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1783 - 1800 |
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| Autores principales: | , |
| Formato: | diagnostic images pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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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=179554116&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554116 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554116 179554116 179554116 10.1007/s10278-024-01010-3 179554116 ppf: 1783 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique. aug: au: Verma, Prem Kumari Kaur, Jagdeep affil: https://ror.org/03xt0bg88 Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, 144008, Jalandhar, Punjab, India sug: subj: Retina Blood Supply Blood Vessels Anatomy and Histology Retina Radiography Retina Anatomy and Histology Image Processing, Computer Assisted Deep Learning Artificial Intelligence Technology Algorithms Neural Networks (Computer) Augmented Reality Machine Learning Retinal Diseases Optical Imaging Robotics Digital Compression ab: Image segmentation is a crucial task in computer vision and image processing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compression, among others. In light of this, the widespread popularity of deep learning (DL) and machine learning has inspired the creation of fresh methods for segmenting images using DL and ML models respectively. We offer a thorough analysis of this recent literature, encompassing the range of ground-breaking initiatives in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid-based methods, recurrent networks, visual attention models, and generative models in adversarial settings. We study the connections, benefits, and importance of various DL- and ML-based segmentation models; look at the most popular datasets; and evaluate results in this Literature. pubtype: Academic Journal doctype: diagnostic images pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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