Two algorithms for segmentation of cell images are proposed. They have a unique part that contains computation of morphological gradient to extract object borders and thinning the obtained borders to get a line of one-pixel thickness. For this task, we propose the fast gray-scale thinning algorithm that is based on the idea of the analysis of binary image layers. Then, the obtained one-pixel lines are used to extract cells and compute their characteristics. The algorithms based on morphological and split/merge segmentation are developed and used for this task.

A new mathematical method is presented for processing and analyzing microscopic images of the epithelium posterius (endothelium) in the human eye cornea. As initial data, we use images of endothelial

This article presents the results of the joint work of image analysis specialists and ophthalmologists on the problem of analyzing images obtained by optical coherence tomography (angiography). A descriptive algorithmic scheme for analyzing images obtained using optical coherence tomography angiography was constructed to automate detection of pathological changes in the morphometric characteristics of the fundus. The algorithmic scheme is based on the image processing, analysis, and recognition methods. A feature space has been developed that makes it possible to identify pathological changes in the structure of the vascular plexuses of the human retina. It was possible to achieve high-accuracy classification of images of healthy eyes and those with pathology. Analysis of correlation between the features and investigated structural, clinical, and functional parameters can be used to confirm that the selected features are informative diagnostic and prognostic markers, and automated analysis using the proposed algorithm makes it possible to speed up and optimize the procedure for interpreting images of this class. Software has been created that allows, with high accuracy in an autonomous mode, performing differential diagnostics of the normal state of vessels from the pathological one, which increases its diagnostic value. In the future, more detailed work is planned with “borderline” conditions, which constitute the main problem in screening patients with diabetes mellitus.

With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan urban ventilation corridors and improve urban ventilation environment is an effective way to solve these problems. In this paper, we use unmanned aerial vehicle (UAV) tilt photography technology to obtain high-precision remote sensing image digital elevation model (DEM) and digital surface model (DSM) data, count the city’s dominant wind direction in each season using long-term meteorological data, and use building height to calculate the dominant wind direction. The projection algorithm calculates the windward area density of this dominant direction. Under the guidance of K-means, the binarized windward area density map is used to determine each area and boundary of potential ventilation corridors within the threshold range, and the length and angle of each area’s fitted elliptical long axis are calculated to extract the ventilation corridors that meet the criteria. On the basis of high-precision stereo remote sensing data from UAV, the paper uses image classification, segmentation, fitting, and fusion algorithms to intelligently mine potential urban ventilation corridors, and the effectiveness of the proposed method is demonstrated through a case study in Zhuji City, Zhejiang Province.