Development of a Technique for the Spectral Description of Curves of Complex Shape for Problems of Object Classification

Vascular pathology symptoms can be determined by retinal image segmentation and classification. However, the retinal images from non-invasive diagnostics have a complex structure containing tree-like vascular beds, multiple segment boundaries, false segments, and various distortions. It should be noted that complex structure images’ segmentation does not always provide a single solution. Thus, the goal is to increase the efficiency of vascular diagnostics. This study aims to develop a technique for describing the geometric properties of complexly structured image segments used for classifying vascular pathologies based on retinal images. The advantages and disadvantages of the existing methods and algorithms of segmentation were considered. The most effective use areas of the mentioned methods and algorithms are revealed. Through detecting retinal thrombosis, the algorithm's efficiency for constructing a mathematical model of an arbitrary shape segment based on the morphological processing of binary and halftone images was justified. A modified variant of this algorithm based on the spectral analysis procedure of arbitrary shape boundary curves was used for the spectral description of complex shape curves for classifying vascular pathologies based on retinal images. Two approaches have been developed. The first one allows obtaining a closing segment of the curve from a symmetric mapping of the initial parametric curves. The second involves intelligent data processing and obtaining contours of minimum thickness, forming convex sets. The results of experiments confirm the possibility of practical use of the developed technique to solve problems of vascular pathology classification based on retinal images, showing the correct forecast probability was 0.93 with all associated risk factors.