Hybrid Intelligent System for Diagnosing Breast Pre-Cancerous and Cancerous Conditions Based on Image Analysis

Modern diagnostic technologies are automated microscopy systems (AMSs). In this research study, the authors analyzed the modern AMS methods and algorithms. Criteria-based comparative analysis of AMS has been made, and their advantages and disadvantages have been identified at the three main levels of image processing. This allowed determining the main direction of such systems development, that is, designing the hybrid intelligent AMS. The work of an expert physician implies visual image interpretation, selection of qualitative features of micro-objects, the formation of diagnostic rules based on expert knowledge, and making diagnoses. Knowledge introduction model contains a productive model, in which knowledge is presented in the form of rules expressed in productive pseudo code if-then. Logic inference machine is a module designed to logically derive the facts and rules from the base according to the laws of formal logic. A set of modern methods and algorithms for low-, mid-, and high-level image processing have been used in the AMS structure

Розробка метрики і методів кількісної оцінки сегментації біомедичних зображень

We analyzed modern digital microscopy. In order to categorize digital microscopy, the following criteria are introduced: level of automation, software level, the level of application of network technologies. To quantitatively estimate the quality of image segmentation, we devised the technique based on a metric approach using the Fréchet and Hausdorff metrics. Modern algorithms for calculating the Hausdorff and Fréchet distances were analyzed.We have introduced the Fréchet distance between trees. It was proven that the Fréchet distance between trees is a metric. We devised a method for estimating a distance between trees of the non-convex regions, based on finding skeletons of regions and determining the distance between them. The algorithm for finding the Hausdorff distance between the non-convex regions is described. We constructed the algorithm for finding a distance between the non-convex regions based on the Fréchet metric between trees.The developed algorithms are included into a hybrid intelligent system for automated microscopy, which is designed to process histological and cytological images.The algorithms were tested using the results of segmentation of histologic and cytologic images from a database as an exampleВведено расстояние Фреше между деревьями и доказано, что это расстояние является метрикой. Разработан метод и алгоритмы определения расстояния между не выпуклыми областями. Спроектирован и программно реализован модуль определения расстояния Фреше между скелетонами. Исследованы погрешности результатов сегментации для метрик Хаусдорфа и Фреше между деревьями на примере биомедицинских изображенийВведено відстань Фреше між деревами та доведено, що ця відстань є метрикою. Розроблено метод і алгоритми визначення відстані між не опуклими областями. Спроектований і програмно реалізований модуль визначення відстані Фреше між скелетонами. Досліджено похибки результатів сегментації для метрик Хаусдорфа та Фреше між деревами на прикладі біомедичних зображен

Розроблення моделей та засобів серверної частини системи обліку пасажиропотоку громадського транспорту “розумного” міста

We built a structure of the server part of the system for passenger traffic registration of city public transport. The developed structure is based on a module principle, which provides simple and fast replacement of particular module in case of its failure. As a result, improved reliability of the system as a whole is achieved, as well as smooth scaling and an increase in the system capacity in future. The algorithm of functioning of the server part of the system for passenger traffic registration of city public transport is developed. Its features are the use of systemic approach to the implementation of incoming data processing and the automation of work of the human-machine system. This made it possible to check correctness of the initial processed data and clearly represent results of calculation of passenger traffic parameters. We developed and implemented specialized software for the server part of the system for passenger traffic registration of public transport. The software is based on the three-level model and implements all the above-mentioned features of the device. Specialized software employs modern approaches of object-oriented programming, including the use of Web frameworks. An information model is developed that ensures reliable data exchange between a client and a server of the system. The model includes a range of modern technologies and protocols. These technologies include video data collection using IP cameras, data transfer with the help of 3G, storing them in a relational DB and on disk space of FTP Server, data processing using the list data structures and storing of statistics in the form of XML files. Accordingly, the developed software is based on the application of modern protocols for the collection, transmission, processing and storage of data (TCP/IP, MySQL Client/Server Protocol, HTTP, FTP, etc.). The technologies and protocols applied allow us to effectively organize the transfer and processing of incoming video, photo- and text data.We developed and implemented technical provision of the server part of the system for passenger traffic registration of public transport. This product provides low cost of technical solution and is based on the use of low-cost components that ensure reliable operation of the system in full.Results of using developed system are presented, in particular: reports that are generated using the developed system that reflect a full picture of the passenger traffic along the vehicle route. The designed and developed system for passenger traffic registration of public transport is verified at ATP "Mens-Auto" and "Etalon" in the city of Ternopil (Ukraine). Obtained data allow us to state that the system operates properly and correctly.Разработана структура серверной части системы учета пассажиропотока общественного транспорта, алгоритм функционировании и специализированное программное обеспечение. Разработано и реализовано информационное и техническое обеспечение, обеспечивающее низкую цену проектного решения и являющееся оптимальным решением с широкими функциональными возможностями. Приведены результаты параметров учета пассажиропотока общественного транспорта, полученных с использованием разработанной системыРозроблено структуру серверної частини системи обліку пасажиропотоку громадського транспорту, алгоритм функціонування та спеціалізоване програмне забезпечення. Розроблено і реалізовано інформаційне та технічне забезпечення, що забезпечує низьку ціну проектного рішення та є оптимальним рішенням з широкими функціональними можливостями. Наведено результати параметрів обліку пасажиропотоку громадського транспорту, отриманих з використанням розробленої систем

An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image

The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, and a cell proliferation biomarker. The authors developed a breast cancer diagnosis method based on immunohistochemical image analysis. The proposed method consists of algorithms for image preprocessing, segmentation, and the determination of informative indicators (relative area and intensity of cells) and an algorithm for determining the molecular genetic breast cancer subtype. An adaptive algorithm for image preprocessing was developed to improve the quality of the images. It includes median filtering and image brightness equalization techniques. In addition, the authors developed a software module part of the HIAMS software package based on the Java programming language and the OpenCV computer vision library. Four molecular genetic breast cancer subtypes could be identified using this solution: subtype Luminal A, subtype Luminal B, subtype HER2/neu amplified, and basalt-like subtype. The developed algorithm for the quantitative characteristics of the immunohistochemical images showed sufficient accuracy in determining the cancer subtype “Luminal A”. It was experimentally established that the relative area of the nuclei of cells covered with biomarkers of progesterone, estrogen, and oncoprotein was more than 85%. The given approach allows for automating and accelerating the process of diagnosis. Developed algorithms for calculating the quantitative characteristics of cells on immunohistochemical images can increase the accuracy of diagnosis

Regions Matching Algorithms Analysis to Quantify the Image Segmentation Results

In the article the matching algorithms of region images are analyzed. The work also presents their advantages and disadvantages. The comparing images algorithm of regions is developed on the basis of the measured chords. The comparison regions algorithms are used to evaluate segmentation algorithms in the Gromov–Hausdorff metric. The algorithm of metric evaluation is developed as an example of biomedical images segmentation

Fuzzy System of Diagnosing in Oncology Telemedicine

Breast cancer is the most common cancer among women. Cytological images are used to diagnose the breast cancer. In this thesis the fuzzy system of diagnosing of precancerous and cancerous conditions of the breast is proposed. This system is based on the expert evaluation of cytological images and can be used in modern oncology telemedicine

An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image

The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, and a cell proliferation biomarker. The authors developed a breast cancer diagnosis method based on immunohistochemical image analysis. The proposed method consists of algorithms for image preprocessing, segmentation, and the determination of informative indicators (relative area and intensity of cells) and an algorithm for determining the molecular genetic breast cancer subtype. An adaptive algorithm for image preprocessing was developed to improve the quality of the images. It includes median filtering and image brightness equalization techniques. In addition, the authors developed a software module part of the HIAMS software package based on the Java programming language and the OpenCV computer vision library. Four molecular genetic breast cancer subtypes could be identified using this solution: subtype Luminal A, subtype Luminal B, subtype HER2/neu amplified, and basalt-like subtype. The developed algorithm for the quantitative characteristics of the immunohistochemical images showed sufficient accuracy in determining the cancer subtype “Luminal A”. It was experimentally established that the relative area of the nuclei of cells covered with biomarkers of progesterone, estrogen, and oncoprotein was more than 85%. The given approach allows for automating and accelerating the process of diagnosis. Developed algorithms for calculating the quantitative characteristics of cells on immunohistochemical images can increase the accuracy of diagnosis