EVALUATION METHODS OF IMAGE SEGMENTATION QUALITY

Abstract

Context. The basic methods of quantitative evaluation of image segmentation quality are explored. They are used to select segmentation algorithms for specific image classes. The object of the study is cytological and histological images that are used in diagnosing the pathological processes in oncology. The subject of the study is quantitative methods for segmentation algorithms’ quality evaluation. Objective. The purpose of the work is to introduce the Gromov-Fr chet metric and develop a metric-based method for quantitative evaluation of segmentation quality for image segmentation algorithms’ comparison. Method. The quantitative evaluation criteria, which are based on comparison with etalon image and without the comparison with etalon image, are analyzed. The algorithms for measuring the distances between images based on the Fr chet, Hausdorff, and Gromov-Hausdorff metrics are analyzed. To calculate the distance between the contours of images, the Gromov-Fr chet distance was introduced. The condition of identity, symmetry and triangle is proved, and it is shown that the Gromov-Fr chet distance is a metric. The metric-based method of quantitative evaluation of segmentation quality is developed. It is based on the use of the Gromov-Hausdorff and Gromov-Fr chet metrics. The method is based on the algorithms for non-convex-into-convex polygon transformation, weighted chord algorithm, and algorithms for calculating the Fr chet and Hausdorff distances. To calculate the Hausdorff distance between convex regions, the Atalah’s algorithm was used. The Thierry and Manillo algorithm was used to find the discrete Fr chet distance. These algorithms have the lowest computational complexity among their class of algorithms. Results. The Gromov-Fr chet metric was introduced and the metric-based method of quantitative evaluation of segmentation quality was developed. Conclusions. The conducted experiments on the basis of cytological images confirmed the performance of software for evaluation the distances between images. The developed method showed a high accuracy of estimation the distances between images. The developed software module was used in intelligence systems for diagnosing the breast precancerous and cancerous conditions. The software can be used in various software systems of computer vision. Promising areas for further research are search for new metrics to evaluate the distances between images