Pages that link to "Item:Q2239127"
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The following pages link to Retina blood vessels segmentation based on the combination of the supervised and unsupervised methods (Q2239127):
Displaying 12 items.
- Automatic segmentation of blood vessels from retinal fundus images through image processing and data mining techniques (Q301762) (← links)
- A quantum mechanics-based algorithm for vessel segmentation in retinal images (Q332011) (← links)
- Retinal vessel segmentation using a finite element based binary level set method (Q479859) (← links)
- Retinal vessel segmentation using a probabilistic tracking method (Q663350) (← links)
- An automatic cognitive graph-based segmentation for detection of blood vessels in retinal images (Q1793589) (← links)
- Retinal blood vessel segmentation from fundus image using an efficient multiscale directional representation technique bendlets (Q1979626) (← links)
- Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and expectation maximization (Q2007565) (← links)
- Retinal blood vessel segmentation based on densely connected U-net (Q2038699) (← links)
- Bayesian method with spatial constraint for retinal vessel segmentation (Q2262165) (← links)
- Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search (Q2418332) (← links)
- Retinal vessel segmentation based on deep forest (Q4996531) (← links)
- (Q5320942) (← links)
