Decision-making support system for the personalization of retinal laser treatment in diabetic retinopathy

In this work, we propose a decision-making support system for automatically mapping an effective photocoagulation pattern for the laser treatment of diabetic retinopathy. The purpose of research to create automated personalization of diabetic macular edema laser treatment. The results are based on analysis of large semi-structured data, methods and algorithms for fundus image processing. The technology improves the quality of retina laser coagulation in the treatment of diabetic macular edema, which is one of the main reasons for pronounced vision decrease. The proposed technology includes original solutions to establish an optimal localization of multitude burns by determining zones exposed to laser. It also includes the recognition of large amount of unstructured data on the anatomical and pathological locations' structures in the area of edema and data optical coherent tomography. As a result, a uniform laser application on the pigment epithelium of the affected retina is ensured. It will increase the treatment safety and its effectiveness, thus avoiding the use of more expensive treatment methods. Assessment of retinal lesions volume and quality will allow predicting the laser photocoagulation results and will contribute to the improvement of laser surgeon's skills. The architecture of a software complex comprises a number of modules, including image processing methods, algorithms for photocoagulation pattern mapping, and intelligent analysis methods.This work was funded by the Russian Foundation for Basic Research under RFBR grant # 19-29-01135 and the Ministry of Science and Higher Education of the Russian Federation within a government project of FSRC “Crystallography and Photonics” RAS

Industrial application of Big Data services in digital economy

Despite multiple positive results in research and development of Big Data technologies, their practical implementation and use remain challenging. At the same time most prominent trends of digital economy require Big Data analysis in various problem domains. Based on generalization of theoretical research and a number of real economy projects in this area there is proposed in this paper an architecture of a software development kit that can be used as a solid platform to build industrial applications. Examples are given for automobile industry with a reference of Industry 4.0 paradigm implementation in practice

Big Data incorporation based on Open Services Provider for distributed enterprises

There is provided a new software solution for multiple data sources integration at modern enterprises with distributed organizational structure. Open Services Provider (OSP) is a platform powered by SEC "Open code" that allows developing situational centers for decision making support based on Big Data analysis and visualization. The paper describes a problem of management of modern distributed enterprises, the proposed OSP solution and results of its probation in practice. Research is supported by Big Data engineering center at Samara University.This work was partially supported by the Ministry of education and science of the Russian Federation in the framework of the implementation of the Program of increasing the competitiveness of SSAU among the world’s leading scientific and educational centers for 2013-2020 years; by the Russian Foundation for Basic Research grants (# 15-29- 03823, # 15-29- 07077, # 16-41- 630761; # 16-29- 11698); by the ONIT RAS program # 6 “Bioinformatics, modern information technologies and mathematical methods in medicine” 2017

Data exchange platform for digital economy

This paper describes an experience of the Data exchange software platform practical use supporting the modern trends of Digital Economy. The platform was initially designed for the suppliers and customers of data sources providing the up-to-date technologies of Big Data processing as an online service. The platform is also open for software developers to upload new algorithms and technologies in order to help them to find new areas of application. There is presented architecture and its software implementation for an intermediary online platform capable of collecting, processing and analysis of various datasets. Modern companies being the members of digital economy can use this platform to process their data and produce business analytics. They can become both suppliers and providers of data, as well as develop and upload new customized algorithms. First results were achieved in the area of retail and social media analysis

Integration Issues of Big Data Analysis on Social Networks

Основная статьяNowadays Social Media becomes one of the major providers of Big Data for analysis of users’ behaviour, focus, trends, and deviations. One user can be presented in several social networks by various avatars. Most users have different dynamics of data processing and generation. In order to provide a solution capable to deal with this, there was developed and implemented a software library for integration with a number of social networks. This paper describes the problem, solution architecture and technical details of its implementation supported by the results of simulation and real data analysis for a number of popular social networks.This work was supported by the Federal Agency of Scientific Organizations (agreement No 007- GZ/Ch3363/26)

Development of automatic selection technique of interest regions in lungs x-rays images

В настоящей работе разработана информационная технология автоматического выделения областей интереса на рентгеновских снимках лёгких, основанная на вычислении текстурных признаков и классификации k-средних. По выделенным объектам в отдельных случаях можно характеризовать не только состояние лёгких пациента, но и его параметры: возраст, пол, телосложение и т.д. В процессе работы выявлена зависимость ошибки сегментации от размера окна фрагментации при использовании метода k-средних. В эксперименте использовались как визуальный критерий оценивания качества результата сегментации, так и критерий, основанный на вычислении ошибки кластеризации на большом наборе фрагментированных изображений. Исследование также включало применение методов предобработки изображений. Так исследование показало, что данная технология обеспечивает ошибку выделения ключевых объектов на уровне 26%. Однако при использовании процедуры эквализации ошибка уменьшается до 14%. Представлены ошибки кластеризации изображения рентгеновского снимка для окон фрагментации 12×12, 24×24 и 36×36. We propose a technique for automatic selection technique of interest regions in lungs x-rays images. The relevance of the problem is associated with enhancing the lung disease count. The technique is based on the texture analysis of lungs x-rays images. The automatic selection technique of interest regions is performed using the k-means clustering algorithm and the morphological operations. The best values of image fragmentation dimensions for the image segmentation required for regions of interest are determined herein. The experiment used both a visual criterion for evaluating the quality of the segmentation result, and a criterion based on the calculation of the clustering error on a large set of fragmented images. The study also included the use of image preprocessing techniques. So the study showed that this technology provides an error in the selection of key objects at a level of 26%. However, when using the equalization procedure, the error is reduced to 14%. X-ray image clustering errors for fragmentation windows 12×12, 24×24 and 36×36 are presented.Работа выполнена при частичной поддержке Федерального агентства научных организаций (соглашение № 007-ГЗ/Ч3363/26); Министерства образования и науки РФ в рамках реализации мероприятий Программы повышения конкурентоспособности Самарского Университета среди ведущих мировых научно-образовательных центров на 2013–2020 годы; грантов РФФИ № 16-41-630761, № 17-01-00972, № 18-37-00418; в рамках госзадания по теме № 0026-2018-0102 "Оптоинформационные технологии получения и обработки гиперспектральных данных"

Industrial application of big data services in digital economy

Despite multiple positive results in research and development of Big Data technologies, their practical implementation and use remain challenging. At the same time most prominent trends of digital economy require Big Data analysis in various problem domains. Based on generalization of theoretical research and a number of real economy projects in this area there is proposed in this paper an architecture of a software development kit that can be used as a solid platform to build industrial applications. Examples are given for automobile industry with a reference of Industry 4.0 paradigm implementation in practice

CUDA parallel programming technology application for analyze of big biomedical data based on computation of effectiveness features

В настоящей работе предложена технология анализа биомедицинских больших данных, основанная на применении технологии CUDA. Технология применялась для анализа большого набора изображений глазного дна, по которым проводилась автоматическая диагностика диабетической ретинопатии. Разработан высокопроизводительный алгоритм, вычисляющий эффективные текстурные признаки для анализа медицинских изображений. В процессе автоматической диагностики на изображении выделяются следующие классы: тонкие сосуды, толстые сосуды, экссудаты и здоровая область. Было проведено исследование эффективности разработанного алгоритма на изображениях размерностей 500х500-1000х1000 пикселей с использованием квадратного окна размерностью 12х12. Продемонстрирована зависимость ускорения разработанного высокопроизводительного алгоритма от различных размеров данных. Как показало исследование, на эффективность алгоритма могут влиять определённые характеристики изображения: чёткость изображения, форма зоны экссудатов, вариабельность сосудов, расположение зрительного диска. This research presents a biomedical big data analysis technology based on CUDA. It uses a parallel algorithm that calculates effective textural attributes of medical images. This technology analyses a large set of an ocular fundus images in purpose for automatic diabetic retinopathy diagnosis. The process of automatic diagnosis defines the classes, such as the thin vessels, the thick vessels, the exudates and the healthy area. During the research the efficiency of the developed algorithm was examined on the images with 500x500 up to 1000x1000 pixel dimensions using a square window with 12x12 pixel dimension. There was demonstrated the values of the developed algorithm acceleration for different amounts of data. As it shows the algorithm efficiency depends on the different fundus image features like the image clarity, the shape of the exudate zone, the vascular variability and the optic disc location.Работа выполнена при финансовой поддержке Российского фонда фундаментальных исследований (гранты № 16-41-630761, № 17-01-00972, № 18-37-00418), государственного задания 3.3025.2017/4.6 и Министерства науки и высшего образования Российской Федерации, в рамках выполнения работ по государственному заданию ФНИЦ «Кристаллография и фотоника» РАН (соглашение №007-ГЗ/Ч3363/26)