Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification

By Sohini Roychowdhury, Dara D. Koozekanani, Keshab K. Parhi. IEEE journal of biomedical and health informatics, 2015

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Segmenting Retinal Blood Vessels With Deep Neural Networks (2016, methodology): “In [17], a morphology-based method detects the evident vessels and background areas, while a Gaussian mixture model classifies the more difficult pixels.”
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, unknown): “Similarly, in [10], [12], [13], and [53], the scores provided by different classifiers are thresholded using the parameter that maximizes the average Acc on the test data (DRIVE test set, STARE and CHASEDB1 in the case of [12], [13], and [53], and DRIVE test set and STARE in [10]).”