The paper proposes a technology for effective feature selection to localize individual characteristics of anatomical and pathological structures in the human eye fundus. Such an approach allows the intellectual analysis of features to be conducted using color subspaces and the regions of interest to be identified. This problem is relevant because in this way the efficiency of laser coagulation surgery can be improved. The technology is based on the texture analysis of certain image patterns.

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... initial textural attributes are derived from different statistical image descriptors calculated using the MaZda library (image histogram, image gradient, series length and adjacency matrices). The analysis of the feature space informativity and selection of the most effective features are carried out using the discriminant data analysis. The best-size image fragmentation windows for eye fundus clustering and sets of features that provide the necessary accuracy in identifying the regions of interest were derived via analyzing the following four image classes: exudates, thick vessels, thin vessels, and healthy areas. The feature selection technology was based on clustering using a K-means method, with the Euclidean and Mahalanobis distance used as a similarity measure. The required minimum size of the fragmentation window and the similarity measure were chosen from a criterion of the minimum clustering error among all the smallest window sizes. The article also presents a system for automatically forming a coagulate plan, expected to be used to support the decision-making during laser retinal coagulation surgery in the treatment of diabetic macular edema. This system is currently being developed based on the proposed technology.