The paper presents a comparative analysis and evaluation of methods for segmenting optical fundus images in order to study their efficiency, accuracy, completeness, and computational complexity in Matlab. The methods analyzed are Otsu, adaptive thresholding, Watershed, K-means, maximum expectation algorithm (EM), and Frangi method. The features, advantages and disadvantages in the context of application for the diagnosis of fundus diseases are considered.
No takes yet. Share an insight, caveat, or question.
Andrikevych et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: