Труды сотрудников ИВМ СО РАН

[Text] / N.I. Kosarev, N.Y. Shaparev // J. Phys. B-At. Mol. Opt. Phys. - 2011. - Vol. 44, Is. 19. - Ст. 195402, DOI 10.1088/0953-4075/44/19/195402. - Cited References: 17 . - ISSN 0953-4075РУБ Optics + Physics, Atomic, Molecular & Chemical

Аннотация: Imprisonment of resonant radiation in an expanding sphere on the basis of the numerical solution of the rate balance equation for excited atoms and the transfer resonance radiation equation is investigated. Calculations of the escape factor for sphere at Doppler form of absorption and scattering profiles are executed. The effect of spectral shift of the absorption (emission) profile relative to the stationary one due to expansion of the medium is taken into account for the case when differential motion is described by velocity that varies linearly with the radius. The behaviour of the escape factor for the sphere is compared with the Sobolev theory. The numerical date for time dependence of intensity of afterglow and contour of the spectral line of leaving outside resonance radiation and spatial distribution of the excited atomic concentration are submitted as well.


Доп.точки доступа:
Shaparev, N.Ya.; Шапарев, Николай Якимович; Косарев Н.И.

/ T. N. Ivanilova // Annual Review in Automatic Programming. - 1985. - Vol. 12, Is. PART 2. - P185-188 . - ISSN 0066-4138
Аннотация: Average measure simulation of forest fire spread is one of the applications of set probability theory. Probability spread calculations are carried out using set identification methods of a model of random spread, taking into account initial data of fire condition in a present moment of time we get prognosis - average measure fire contour - in any post coming moment of time. В© 1985.

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Держатели документа:
Computer Center, SB AS USSR, Krasnoyarsk, 660036, USSR
ИВМ СО РАН

Доп.точки доступа:
Ivanilova, T.N.

[Text] / L. Cadena [et al.] // INTERNATIONAL CONFERENCE ON OPTICAL AND PHOTONIC ENGINEERING (ICOPEN. - 2015. - Vol. 9524: 3rd Conference of the Optics-and-Photonics-Society-of-Singapore / (APR 14-16, 2015, Singapore, SINGAPORE). - Ст. 95242W. - (Proceedings of SPIE), DOI 10.1117/12.2189725. - Cited References:17 . - ISSN 0277-786XРУБ Engineering, Electrical & Electronic + Optics + Physics, Applied

Аннотация: Contour detect in the urology medical image. The investigation algorithm FFST revealed that the contours of objects can be obtained as the sum of the coefficients shearlet transform a fixed value for the last scale and the of all possible values of the shift parameter. The results of this task using a modified algorithm FFST for data processing urology image is show. In the results of the corresponding calculations for some images and a comparison with filters Sobel and Prewitt. Shows the relevant calculations for some images and a comparison with Sobel and Prewitt filters respectively.

WOS,
Scopus

Держатели документа:
Univ Fuerzas Armadas ESPE, Sangolqui, Ecuador.
Colegio Fiscal Eloy Alfaro, Quito, Ecuador.
Inst Computat Modelling SB RAS, Krasnoyarsk 660036, Russia.
Novosibirsk State Univ, Novosibirsk 630090, Russia.

Доп.точки доступа:
Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Rios, Ramiro; Simonov, Konstantin; Симонов, Константин Васильевич; Romanenko, Alexey

/ N. Y. Shaparev // Dokl. Phys. - 2015. - Vol. 60, Is. 11. - P479-482, DOI 10.1134/S102833581511004X . - ISSN 1028-3358
Аннотация: Absorption of external radiation in self-similar expanding gaseous sphere is considered. The dependence of the optical thickness of the medium, contour shape, and width of the spectral line of absorption on the initial optical thickness and ratio of the boundary gas spread velocity to the thermal velocity of atoms is determined. © 2015, Pleiades Publishing, Ltd.

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WOS

Держатели документа:
Institute of Computational Modelling, Siberian Branch, Russian Academy of Sciences, Akademgorodok 50, str. 44, Krasnoyarsk, Russian Federation
National Research Tomsk State University, pr. Lenina 36, Tomsk, Russian Federation

Доп.точки доступа:
Шапарев, Николай Якимович
Свободных экз. нет

/ A. Zotin [et al.] // Computer Vision in Control Systems-4 : Springer Science and Business Media Deutschland GmbH, 2018. - Vol. 136. - P223-259, DOI 10.1007/978-3-319-67994-5_9 . -
Аннотация: Image processing techniques play an important role in the diagnostics and detection of diseases and monitoring the patients having these diseases. The chapter presents the medical image processing and morphological analysis in the solution of urology and plastic surgery (hernioplasty) problems. Novel methodology for processing medical images using a color coding of contour representation obtained by Digital Shearlet Transform (DST) has been presented. The object contours in the medical urology images are obtained using the conventional filters, and then results are compared. Since medical images can contain some noise, it makes sense to suppress the noise at the preprocessing step. For this purpose, the optimized in implementation algorithms of the most frequently used filters, such as the mean filter, Gaussian filter, median filter, and 2D cleaner filter, had been developed. A comparison of the optimized and ordinary implementations of noise reduction filter shows great speed improvement of the optimized implementations (around 3–20 times). Additionally, the parallel implementation gives 2–3.5 times performance boost. The proposed methodology allows to improve the accuracy and decrease the error of the sought parameters and characteristics by 10–20% on average without a lack of significant details in the structural features of the examined objects. The results of the experimental study show an error decrease in data representation for the plastic surgery (hernioplasty) by 15–25%. © 2018, Springer International Publishing AG.

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Держатели документа:
Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy av., Krasnoyarsk, Russian Federation
Institute of Computational Modeling of the Siberian Branch of the Russian Academy of the Sciences, 50/44 Akademgorodok, Krasnoyarsk, Russian Federation
V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, 1 Partizana Geleznyaka St., Krasnoyarsk, Russian Federation
Siberian Federal University, 79 Svobodny av., Krasnoyarsk, Russian Federation
Universidad de las Fuerzas Armadas ESPE, Av. Gral Ruminahui s/n, Sangolqui, Ecuador

Доп.точки доступа:
Zotin, A.; Simonov, K.; Kapsargin, F.; Cherepanova, T.; Kruglyakov, A.; Cadena, L.

/ L. Cadena [et al.] // Lecture Notes in Engineering and Computer Science : Newswood Limited, 2017. - Vol. 2227: 2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017 (15 March 2017 through 17 March 2017, ) Conference code: 133365. - P401-404 . -
Аннотация: A review of the basic concepts shearlet transform spatial data observations. The possibilities of the new approach for the geometric analysis of complex medical images. The proposed method can improve the radiological diagnosis of urological diseases by detailing changes of tissues. On the basis of the developed method of spectral data decomposition is performed solution of filtration problem and isolating contour studied medical target. The task of image contrast is also solved for the better understanding of the found geometric features and patterns.

Scopus

Держатели документа:
Electric and Electronic Department, Universidad de las Fuerzas Armadas ESPE, Av. Gral Ruminahui s/n, Sangolqui, Ecuador
College Juan Suarez Chacon, Quito, Ecuador
Siberian Federal University, 79 Svobodny pr., Krasnoyarsk, Russian Federation
Institute of Computational Modelling, Siberian Branch, Russian Academy of Science, 50/44 Akademgorodok str., Krasnoyarsk, Russian Federation
Krasnoyarsk State Medical University, 1 Partizana Geleznyaka str., Krasnoyarsk, Russian Federation

Доп.точки доступа:
Cadena, L.; Cadena, F.; Kruglyakov, A.; Simonov, K.; Kapsargin, F.

[Текст] : статья / Юсиф Ахмед Хамад [и др.] // Медицина и высокие технологии. - 2018. - № 3. - С. 5-13 . - ISSN 2306-3645
   Перевод заглавия: COMPUTATIONAL PROCESSING TECHNIQUE MEDICAL IMAGES USING WAVELET AND NEURAL NETWORKS

УДК

004.932

Аннотация: В статье представлен подход к диагностике опухоли молочной железы - вычислительная методика поэтапной классификации с использованием искусственной нейронной сети (машинное обучение) и выявление опухоли молочной железы для медицинской визуализации с помощью методов пороговой сегментации и метода нечеткой кластеризации С-средних.
This paper presents an innovative approach to the diagnosis of breast tumor - a computational methodology for stage classification using artificial neural network (learning machine) and to detect Breast Tumor through thresholding and fuzzy C-means clustering methods for medical imaging application.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН
Институт космических и информационных технологий Сибирского федерального университета

Доп.точки доступа:
Хамад, Юсиф Ахмед; Hamad Yousif Ahmed; Кириллова, Светлана Владимировна; Kirillova Svetlana Vladimirovna; Курако, Михаил Александрович; Kurako Mikhail Aleksandrovich; Симонов, Константин Васильевич; Simonov Konstantin Vasilyevich

/ Y. A. Hamad, K. Simonov, M. B. Naeem // 2018 1ST ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION AND SCIENCES : IEEE, 2018. - 1st Annual International Conference on Information and Sciences (AiCIS) (NOV 20-21, 2018, Univ Fallujah, Fallujah, IRAQ). - P51-57, DOI 10.1109/AiCIS.2018.00022. - Cited References:22 . - ISBN 978-1-5386-9188-5РУБ Engineering, Multidisciplinary + Multidisciplinary Sciences

Аннотация: Image processing techniques play an important role in the diagnostics and detection of diseases and monitoring the patients having these diseases. Breast Cancer detection of medical images is one of the most important elements of this field. Because of low contrast and ambiguous the structure of the tumor cells in breast images, it is still a challenging task to automatically segment the breast tumors. Our method presents an innovative approach to the diagnosis of breast tumor incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using balance contrast enhancement techniques (BCET). The results of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method and Thresholding method to segment the out boundaries of the breast and to locate the Breast Tumor boundaries (shape, area, spatial sizes, etc.) in the images. The third stage feature extraction using Discrete Wavelet Transform (DWI). Finally the artificial neural network will be used to classify the stage of Breast Tumor that is benign, malignant or normal. The early detection of Breast tumor will improves the chances of survival for the patient Probabilistic Neural Network (PNN) with radial basis function will be employed to implement an automated breast tumor classification. The simulated results shown that classifier and segmentation algorithm provides better accuracy than previous method. Proper segmentation is mandatory for efficient feature extraction and classification.

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Источник статьи

Держатели документа:
Siberian Fed Univ, Inst Space & Informat Sci, Krasnoyarsk, Russia.
Russian Acad Sci, Siberian Branch, Inst Computat Modeling, Krasnoyarsk, Russia.
Al Maaref Univ Coll, Dept Comp Sci, Ramadi, Iraq.

Доп.точки доступа:
Hamad, Yousif A.; Simonov, Konstantin; Naeem, Mohammad B.