The methods of processing biomedical images, namely thermal images, are investigated. Algorithms for calculating the temperature and area of the zone of interest in the manual mode operator-computer, as well as in the automatic mode, are specified. Methods of thermal image processing are presented, namely recursive generalized contour preparation and preparation based on histograms of connections. An experimental study of these methods was performed, as well as a comparison of thermal image segmentation methods in manual segmentation modes, using contour preparation-based segmentation, multilevel segmentation based on recursive generalized contour preparation, and automatic segmentation based on connectivity histograms.

This paper focuses on aerospace image analysis methods. Aerospace images are considered for the study of agricultural crops of northern Kazakhstan belonging to the A. I. Barayev Research and Production Center for Grain Farming. The main goal of the research is the development and implementation of algorithms that make it possible to detect and highlight on aerospace images the factors that negatively affect the growth of crops over the growing seasons. To resolve the problem, the spectral brightness coefficient (SBC), NDVI, clustering, orthogonal transformations are used. Special attention was paid to the development of software tools for selecting characteristics that describe texture differences to segment texture regions into sub-regions. That is, the issue of the applicability of sets of textural features and orthogonal transformations for the analysis of experimental data to identify characteristic areas on aerospace images that can be associated with weeds, pests, etc. in the future was investigated. The questions of signal image processing remain the focus of attention of different specialists. The images act both as a result and as a research object in physics, astronautics, meteorology, forensic medicine and many other areas of science and technology. Furthermore, image processing systems are currently being used to resolve many applied problems. A program has been implemented in the MATLAB environment that allows performing spectral transformations of six types: 1) cosine; 2) Hadamard of order 2n; 3) Hadamard of order n=p+1, p≡3 (mod4); 4) Haar; 5) slant; 6) Daubechies 4. Analysis of the data obtained revealed the features of changes in the reflectivity of cultivated crops and weeds in certain periods of the growing season. The data obtained are of great importance for the validation of remote space observations using aerospace images

<span lang="EN-US">This article discusses a large number of textural features and integral transformations for the analysis of texture-type images. It also discusses the description and analysis of the features of applying existing methods for segmenting texture areas in images and determining the advantages and disadvantages of these methods and the problems that arise in the segmentation of texture areas in images. The purpose of the ongoing research is to use methods and determine the effectiveness of methods for the analysis of aerospace images, which are a combination of textural regions of natural origin and artificial objects. Currently, the automation of the processing of aerospace information, in particular images of the earth’s surface, remains an urgent task. The main goal is to develop models and methods for more efficient use of information technologies for the analysis of multispectral texture-type images in the developed algorithms. The article proposes a comprehensive approach to these issues, that is, the consideration of a large number of textural features by integral transformation to eventually create algorithms and programs applicable to solving a wide class of problems in agriculture.</span><p> </p>

<p><span lang="EN-US">An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.</span></p>

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