[Текст] : статья / Г. В. Макарская, А. В. Андрианова, С. В. Тарских // Проблемы патологии, иммунологии и охраны здоровья рыб и других гидробионтов. - 2015. - С. 563-570
. - ISBN 978-5-906682-37-6
Перевод заглавия: The interspecific and age features of antioxidant activity of suspended tissue of amphipods from different water-currents
Аннотация: The general and specific features of kinetics of free radicals formations and them elimination in tissue homogenates of amphipods depending on their species belonging, a place and conditions of inhabitation, age are revealed by chemiluminescent analysis of kinetics of oxidative activity in conditions of activation oxidative stress by peroxide of hydrogen in vitro.
РИНЦ,
Источник статьи
Держатели документа:
Институт вычислительного моделирования СО РАН
Красноярский научный центр СО РАН
Научно-исследовательский институт экологии рыбохозяйственных водоемов
Доп.точки доступа:
Андрианова, Анна Владимировна; Andrianova A.V.; Тарских, С.В.; Tarskikh S.V.; Makarskaya G.V.; IV Международная конференция "Проблемы патологии, иммунологии и охраны здоровья рыб и других гидробионтов" (2015 ; 24.09 - 27.09 ; Борок)
Труды сотрудников ИВМ СО РАН
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Межвидовые и возрастные особенности антиоксидантной активности суспензий тканей амфипод из разнотипных водотоков
004.932
О-20
О-20
Обнаружение опухоли мозга на основе мрт с применением метода нечеткой кластеризации С-средних
: статья / Александр Геннадьевич Зотин [и др.] // Медицина и высокие технологии. - 2018. - № 1. - С. 20-28
. - ISSN 2306-3645
Перевод заглавия: Mri brain’s tumor edge detection based on fuzzy c-means
Кл.слова (ненормированные):
детектор границ -- медианный фильтр -- метод нечеткой кластеризации С-средних (FCM) -- методика улучшения контрастности (BCET) -- детектор границ Кэнни -- медицинское изображение -- edge detection -- median filter -- Fuzzy c means (FCM) -- balance contrast enhancement technique (BCET) -- Canny operator -- medical imaging
Аннотация: В настоящее время обработка медицинских изображений является наиболее сложной и развивающейся областью. При этом выявление границ объектов интереса на снимках МРТ является одним из наиболее важных элементов этой области. В настоящей статье предлагается методика обнаружения границ опухоли головного мозга по МРТ пациента. Эта методика включает несколько этапов: во-первых - удаления шума, а затем улучшение медицинского изображения с использованием метода улучшения контрастности (Balance Contrast Enhancement Technique, BCET), во-вторых - сегментация изображения с использованием метода нечеткой кластеризации С-средних (Fuzzy c-Means, FCM), и наконец, в-третьих, применение детектора Кэнни для выявления тонких границ. Для экспериментального исследования использованы изображения, содержащие опухоли головного мозга, которые характеризовались разным особенностями: расположением, типом патологии, формой, размером и плотностью, а также размером площади пораженной ткани около опухолевого пространства. Обнаружение и выделение опухоли на снимках МРТ головного мозга осуществлялось с использованием программного обеспечения MATLAB. Результат исследований экспериментального материала с использованием предлагаемой методики демонстрирует достаточно хорошую устойчивость к шуму. Кроме того, было обнаружено, что повышение точности решения задач геометрического анализа и сегментации, в некоторых случаях опухолевой патологии, на 10-15% лучше, чем соответствующие оценки экспертов.
Medical image processing is the most challenging and emerging field nowadays. Edge detection of MRI images is one of the most important elements of this field. This paper describes the proposed strategy to detect the edges of brain tumor from patient’s MRI scan images of the brain. This method incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using BCET. The result of second stage is subjected to image segmentation by using Fuzzy c-Means (FCM) clustering method. Finally, Canny edge detection method is applied to detect the fine edges. For the experimental study we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done by using MATLAB software. The result of studies of the experimental material with usage of the proposed methodology demonstrates some resistivity to a noise. Also, an increase in the accuracy of solving the problems of geometric analysis and segmentation, in some cases of tumor pathology, was found to be up to 10-15% better relative to the corresponding expert estimates.
РИНЦ
Держатели документа:
Институт вычислительного моделирования СО РАН
Институт космических и информационных технологий Сибирского федерального университета
Сибирский государственный университет науки и технологии им. академика М.Ф. Решетнева
Доп.точки доступа:
Зотин, Александр Геннадьевич; Zotin Alexander Gennadievich; Хамад, Юсиф Ахмед; Hamad Yousif Ahmed; Кириллова, Светлана Владимировна; Kirillova Svetlana Vladimirovna; Курако, Михаил Александрович; Kurako Mikhail Aleksandrovich; Симонов, Константин Васильевич; Simonov Konstantin Vasilyevich
Перевод заглавия: Mri brain’s tumor edge detection based on fuzzy c-means
| УДК |
Кл.слова (ненормированные):
детектор границ -- медианный фильтр -- метод нечеткой кластеризации С-средних (FCM) -- методика улучшения контрастности (BCET) -- детектор границ Кэнни -- медицинское изображение -- edge detection -- median filter -- Fuzzy c means (FCM) -- balance contrast enhancement technique (BCET) -- Canny operator -- medical imaging
Аннотация: В настоящее время обработка медицинских изображений является наиболее сложной и развивающейся областью. При этом выявление границ объектов интереса на снимках МРТ является одним из наиболее важных элементов этой области. В настоящей статье предлагается методика обнаружения границ опухоли головного мозга по МРТ пациента. Эта методика включает несколько этапов: во-первых - удаления шума, а затем улучшение медицинского изображения с использованием метода улучшения контрастности (Balance Contrast Enhancement Technique, BCET), во-вторых - сегментация изображения с использованием метода нечеткой кластеризации С-средних (Fuzzy c-Means, FCM), и наконец, в-третьих, применение детектора Кэнни для выявления тонких границ. Для экспериментального исследования использованы изображения, содержащие опухоли головного мозга, которые характеризовались разным особенностями: расположением, типом патологии, формой, размером и плотностью, а также размером площади пораженной ткани около опухолевого пространства. Обнаружение и выделение опухоли на снимках МРТ головного мозга осуществлялось с использованием программного обеспечения MATLAB. Результат исследований экспериментального материала с использованием предлагаемой методики демонстрирует достаточно хорошую устойчивость к шуму. Кроме того, было обнаружено, что повышение точности решения задач геометрического анализа и сегментации, в некоторых случаях опухолевой патологии, на 10-15% лучше, чем соответствующие оценки экспертов.
Medical image processing is the most challenging and emerging field nowadays. Edge detection of MRI images is one of the most important elements of this field. This paper describes the proposed strategy to detect the edges of brain tumor from patient’s MRI scan images of the brain. This method incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using BCET. The result of second stage is subjected to image segmentation by using Fuzzy c-Means (FCM) clustering method. Finally, Canny edge detection method is applied to detect the fine edges. For the experimental study we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done by using MATLAB software. The result of studies of the experimental material with usage of the proposed methodology demonstrates some resistivity to a noise. Also, an increase in the accuracy of solving the problems of geometric analysis and segmentation, in some cases of tumor pathology, was found to be up to 10-15% better relative to the corresponding expert estimates.
РИНЦ
Держатели документа:
Институт вычислительного моделирования СО РАН
Институт космических и информационных технологий Сибирского федерального университета
Сибирский государственный университет науки и технологии им. академика М.Ф. Решетнева
Доп.точки доступа:
Зотин, Александр Геннадьевич; Zotin Alexander Gennadievich; Хамад, Юсиф Ахмед; Hamad Yousif Ahmed; Кириллова, Светлана Владимировна; Kirillova Svetlana Vladimirovna; Курако, Михаил Александрович; Kurako Mikhail Aleksandrovich; Симонов, Константин Васильевич; Simonov Konstantin Vasilyevich
Edge detection in MRI brain tumor images based on fuzzy C-means clustering
/ A. Zotin [et al.] // Procedia Computer Science : Elsevier B.V., 2018. - Vol. 126: 22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2018 (3 September 2018 through 5 September 2018, ) Conference code: 141492. - P1261-1270, DOI 10.1016/j.procS.2018.08.069
. -
Кл.слова (ненормированные):
balance contrast enhancement technique -- Canny operator -- Edge detection -- fuzzy C means -- median filter -- medical imaging -- Brain -- Diagnosis -- Edge detection -- Fuzzy filters -- Image segmentation -- Knowledge based systems -- Magnetic resonance imaging -- MATLAB -- Median filters -- Medical imaging -- Pathology -- Tumors -- Canny edge detection -- Canny Operators -- Contrast Enhancement -- Expert estimates -- Fuzzy C mean -- Fuzzy C means clustering -- Matlab- software -- Noise removal -- Image enhancement
Аннотация: Nowadays, medical image processing is the most challenging and emerging field. Edge detection of MRI images is one of the most important stage in this field. The paper describes the proposed strategy to detect the edges of brain tumor from patient's MRI scan images of the brain. At the first stage, this method includes some noise removal functions improving features that provides better characteristics of medical images for reliable diagnosis using Balance Contrast Enhancement Technique (BCET). The result of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method. Finally, Canny edge detection method is applied to detect the fine edgeS. During the experimental study, we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done using MATLAB software. The obtained results demonstrate some resistivity to a noise. Also, the accuracy of segmentation, in some cases of tumor pathology, was increased up to 10-15% regarding the expert estimateS. © 2018 The Author(s).
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Держатели документа:
Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky rabochy av., Krasnoyarsk, 660037, Russian Federation
Institute of Computational Modeling of the Siberian Branch, Russian Academy of Sciences, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation
Siberian Federal University, 79 Svobodny st., Krasnoyarsk, 660041, Russian Federation
Доп.точки доступа:
Zotin, A.; Simonov, K.; Kurako, M.; Hamad, Y.; Kirillova, S.
Кл.слова (ненормированные):
balance contrast enhancement technique -- Canny operator -- Edge detection -- fuzzy C means -- median filter -- medical imaging -- Brain -- Diagnosis -- Edge detection -- Fuzzy filters -- Image segmentation -- Knowledge based systems -- Magnetic resonance imaging -- MATLAB -- Median filters -- Medical imaging -- Pathology -- Tumors -- Canny edge detection -- Canny Operators -- Contrast Enhancement -- Expert estimates -- Fuzzy C mean -- Fuzzy C means clustering -- Matlab- software -- Noise removal -- Image enhancement
Аннотация: Nowadays, medical image processing is the most challenging and emerging field. Edge detection of MRI images is one of the most important stage in this field. The paper describes the proposed strategy to detect the edges of brain tumor from patient's MRI scan images of the brain. At the first stage, this method includes some noise removal functions improving features that provides better characteristics of medical images for reliable diagnosis using Balance Contrast Enhancement Technique (BCET). The result of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method. Finally, Canny edge detection method is applied to detect the fine edgeS. During the experimental study, we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done using MATLAB software. The obtained results demonstrate some resistivity to a noise. Also, the accuracy of segmentation, in some cases of tumor pathology, was increased up to 10-15% regarding the expert estimateS. © 2018 The Author(s).
Scopus,
Смотреть статью
Держатели документа:
Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky rabochy av., Krasnoyarsk, 660037, Russian Federation
Institute of Computational Modeling of the Siberian Branch, Russian Academy of Sciences, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation
Siberian Federal University, 79 Svobodny st., Krasnoyarsk, 660041, Russian Federation
Доп.точки доступа:
Zotin, A.; Simonov, K.; Kurako, M.; Hamad, Y.; Kirillova, S.
Brain's tumor edge detection on low contrast medical images
/ 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). - P45-50, DOI 10.1109/AiCIS.2018.00021. - Cited References:15
. - ISBN 978-1-5386-9188-5
РУБ Engineering, Multidisciplinary + Multidisciplinary Sciences
Аннотация: Medical image processing is the most challenging and emerging field nowadays. Edge detection of MRI images is one of the most important elements of this field. This paper describes the proposed strategy to detect the edges of brain tumor from patient's MRI scan images of the brain. This method incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using BCET. The result of second stage is subjected to image segmentation by using Fuzzy c-Means (ECM) clustering method. Finally, canny edge detection method is applied to detect the fine edges. For the experimental study we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done by using MATLAB software. The result of studies of the experimental material with usage of the proposed methodology demonstrates some resistivity to a noise. Also, an increase in the accuracy of solving the problems of geometric analysis and segmentation, in some cases of tumor pathology, was found to be up to 10-15% better relative to the corresponding expert estimates.
WOS,
Смотреть статью,
Scopus,
РИНЦ,
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.
Рубрики:
SEGMENTATION
MODEL
Кл.слова (ненормированные):
brain tumor -- tumor pathology -- edge detection -- median filter -- fuzzy C -- means -- Balance Contrast Enhancement Technique (BCET) -- Canny operator -- medical imaging
SEGMENTATION
MODEL
Кл.слова (ненормированные):
brain tumor -- tumor pathology -- edge detection -- median filter -- fuzzy C -- means -- Balance Contrast Enhancement Technique (BCET) -- Canny operator -- medical imaging
Аннотация: Medical image processing is the most challenging and emerging field nowadays. Edge detection of MRI images is one of the most important elements of this field. This paper describes the proposed strategy to detect the edges of brain tumor from patient's MRI scan images of the brain. This method incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using BCET. The result of second stage is subjected to image segmentation by using Fuzzy c-Means (ECM) clustering method. Finally, canny edge detection method is applied to detect the fine edges. For the experimental study we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done by using MATLAB software. The result of studies of the experimental material with usage of the proposed methodology demonstrates some resistivity to a noise. Also, an increase in the accuracy of solving the problems of geometric analysis and segmentation, in some cases of tumor pathology, was found to be up to 10-15% better relative to the corresponding expert estimates.
WOS,
Смотреть статью,
Scopus,
РИНЦ,
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.
Breast Cancer Detection and classification Using Artificial Neural Networks
/ 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 978-1-5386-9188-5
РУБ 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.
WOS,
Смотреть статью,
Scopus,
РИНЦ,
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.
Рубрики:
TISSUE
Кл.слова (ненормированные):
Image processing -- Breast Tumors -- Noise Reduction DWT -- PNN-RBF -- Contour -- initialization
TISSUE
Кл.слова (ненормированные):
Image processing -- Breast Tumors -- Noise Reduction DWT -- PNN-RBF -- Contour -- initialization
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.
WOS,
Смотреть статью,
Scopus,
РИНЦ,
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.
Triplet Frequencies Implementation in Total Transcriptome Analysis
/ M. Sadovsky, T. Guseva, V. Biriukov // (8 May 2019 through 10 May 2019 : Springer Verlag, 2019. - Vol. 11465 LNBI. - P370-378, DOI 10.1007/978-3-030-17938-0_33
. -
Кл.слова (ненормированные):
Clustering -- Order -- Probability -- Projection -- Symmetry -- Triplet -- Bioinformatics -- Biomedical engineering -- Crystal symmetry -- Probability -- Clustering -- Mutual entropy -- Order -- Projection -- Tissue specificity -- Tissue specifics -- Transcriptome analysis -- Triplet -- Tissue
Аннотация: We studied the structuredness inA total transcriptome of Siberian larch. To do that, the contigs from total transcriptome has been labeled with the reads comprising the tissue specific transcriptomes, and the distribution of the contigs from the total transcriptome has been developed with respect to the mutual entropy of the frequencies of occurrence of reads from tissue specific transcriptomes. It was found that a number of contigs contain comparable amounts of reads from different tissues, so the chimeric transcripts to be extremely abundant. On the contrary, the transcripts with high tissue specificity do not yield a reliable clustering revealing the tissue specificity. This fact makes usage of total transcriptome for the purposes of differential expression arguable. © 2019, Springer Nature Switzerland AG.
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РИНЦ
Держатели документа:
Institute of Computational Modelling of SB RAS, Akademgorodok, Krasnoyarsk 660036, Russian Federation
Institute of Fundamental Biology and Biotechnology, Siberian Federal University, Svobodny prosp., 79, Krasnoyarsk, 660049, Russian Federation
Доп.точки доступа:
Sadovsky, M.; Guseva, T.; Biriukov, V.
Кл.слова (ненормированные):
Clustering -- Order -- Probability -- Projection -- Symmetry -- Triplet -- Bioinformatics -- Biomedical engineering -- Crystal symmetry -- Probability -- Clustering -- Mutual entropy -- Order -- Projection -- Tissue specificity -- Tissue specifics -- Transcriptome analysis -- Triplet -- Tissue
Аннотация: We studied the structuredness inA total transcriptome of Siberian larch. To do that, the contigs from total transcriptome has been labeled with the reads comprising the tissue specific transcriptomes, and the distribution of the contigs from the total transcriptome has been developed with respect to the mutual entropy of the frequencies of occurrence of reads from tissue specific transcriptomes. It was found that a number of contigs contain comparable amounts of reads from different tissues, so the chimeric transcripts to be extremely abundant. On the contrary, the transcripts with high tissue specificity do not yield a reliable clustering revealing the tissue specificity. This fact makes usage of total transcriptome for the purposes of differential expression arguable. © 2019, Springer Nature Switzerland AG.
Scopus,
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РИНЦ
Держатели документа:
Institute of Computational Modelling of SB RAS, Akademgorodok, Krasnoyarsk 660036, Russian Federation
Institute of Fundamental Biology and Biotechnology, Siberian Federal University, Svobodny prosp., 79, Krasnoyarsk, 660049, Russian Federation
Доп.точки доступа:
Sadovsky, M.; Guseva, T.; Biriukov, V.
Super-efficient laser hyperthermia of malignant cells with core-shell nanoparticles based on alternative plasmonic materials
/ A. S. Kostyukov [et al.] // J. Quant. Spectrosc. Radiat. Transf. - 2019. - Vol. 236. - Ст. 106599, DOI 10.1016/j.jqsrt.2019.106599
. - ISSN 0022-4073
Кл.слова (ненормированные):
Conducting oxides -- Nanoparticle -- Nanoshell -- Plasmonic photothermal therapy -- Aluminum oxide -- Core shell nanoparticles -- Efficiency -- Gallium compounds -- II-VI semiconductors -- Nanoparticles -- Nanoshells -- Nanostructured materials -- Optical films -- Plasmonics -- Pulsed lasers -- Shells (structures) -- Silica -- Specific heat -- Transparent conducting oxides -- Zinc oxide -- Aluminum-doped zinc oxide -- Comparative studies -- Conducting oxides -- Gallium doped zinc oxides -- Nanoshell -- Orders of magnitude -- Photothermal therapy -- Spatial localization -- Plasmonic nanoparticles -- aluminum -- cell -- comparative study -- gold -- nanoparticle -- oxide -- zinc
Аннотация: New type of highly absorbing core-shell AZO/Au (aluminum doped zinc oxide/gold) and GZO/Au (gallium doped zinc oxide/gold) nanoparticles have been proposed for hyperthermia of malignant cells purposes. Comparative studies of pulsed laser hyperthermia were performed for Au nanoshells with AZO core and traditional SiO2 (quartz) core. We show that under the same conditions, the hyperthermia efficiency in the case of AZO increases by several orders of magnitude compared to SiO2 due to low heat capacity of AZO. Similar results have been obtained for GZO core which has same heat capacity. Calculations for pico-, nano- and sub-microsecond pulses demonstrate that reduced pulse duration results in strong spatial localization of overheated areas around nanoparticles, which ensures the absence of negative effects to the normal tissue. Moreover, we propose new alternative way for the optimization of hyperthermia efficiency: instead of maximizing the absorption of nanoparticles, we enhance the thermal damage effect on the membrane of malignant cell. This strategy allows to find the parameters of nanoparticle and the incident radiation for the most effective therapy. © 2019 Elsevier Ltd
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Держатели документа:
Siberian Federal UniversityKrasnoyarsk, Russian Federation
Institute of Computational Modeling SB RASKrasnoyarsk, Russian Federation
Siberian State University of Science and TechnologyKrasnoyarsk, Russian Federation
The Institute of Optics, University of RochesterNY, United States
Kirensky Institute of Physics, Federal Research Center KSC SB RASKrasnoyarsk, Russian Federation
Доп.точки доступа:
Kostyukov, A. S.; Ershov, A. E.; Gerasimov, V. S.; Filimonov, S. A.; Rasskazov, I. L.; Karpov, S. V.
Кл.слова (ненормированные):
Conducting oxides -- Nanoparticle -- Nanoshell -- Plasmonic photothermal therapy -- Aluminum oxide -- Core shell nanoparticles -- Efficiency -- Gallium compounds -- II-VI semiconductors -- Nanoparticles -- Nanoshells -- Nanostructured materials -- Optical films -- Plasmonics -- Pulsed lasers -- Shells (structures) -- Silica -- Specific heat -- Transparent conducting oxides -- Zinc oxide -- Aluminum-doped zinc oxide -- Comparative studies -- Conducting oxides -- Gallium doped zinc oxides -- Nanoshell -- Orders of magnitude -- Photothermal therapy -- Spatial localization -- Plasmonic nanoparticles -- aluminum -- cell -- comparative study -- gold -- nanoparticle -- oxide -- zinc
Аннотация: New type of highly absorbing core-shell AZO/Au (aluminum doped zinc oxide/gold) and GZO/Au (gallium doped zinc oxide/gold) nanoparticles have been proposed for hyperthermia of malignant cells purposes. Comparative studies of pulsed laser hyperthermia were performed for Au nanoshells with AZO core and traditional SiO2 (quartz) core. We show that under the same conditions, the hyperthermia efficiency in the case of AZO increases by several orders of magnitude compared to SiO2 due to low heat capacity of AZO. Similar results have been obtained for GZO core which has same heat capacity. Calculations for pico-, nano- and sub-microsecond pulses demonstrate that reduced pulse duration results in strong spatial localization of overheated areas around nanoparticles, which ensures the absence of negative effects to the normal tissue. Moreover, we propose new alternative way for the optimization of hyperthermia efficiency: instead of maximizing the absorption of nanoparticles, we enhance the thermal damage effect on the membrane of malignant cell. This strategy allows to find the parameters of nanoparticle and the incident radiation for the most effective therapy. © 2019 Elsevier Ltd
Scopus,
Смотреть статью
Держатели документа:
Siberian Federal UniversityKrasnoyarsk, Russian Federation
Institute of Computational Modeling SB RASKrasnoyarsk, Russian Federation
Siberian State University of Science and TechnologyKrasnoyarsk, Russian Federation
The Institute of Optics, University of RochesterNY, United States
Kirensky Institute of Physics, Federal Research Center KSC SB RASKrasnoyarsk, Russian Federation
Доп.точки доступа:
Kostyukov, A. S.; Ershov, A. E.; Gerasimov, V. S.; Filimonov, S. A.; Rasskazov, I. L.; Karpov, S. V.
Evaluating of tissue germination and growth rate of ROI on implants of electron scanning microscopy images
/ Y. Hamad, O. K.J. Mohammed, K. Simonov // ACM International Conference Proceeding Series : Association for Computing Machinery, 2019. - 9th International Conference on Information Systems and Technologies, ICIST 2019 (24 March 2019 through 26 March 2019, ) Conference code: 154766. - Ст. a22, DOI 10.1145/3361570.3361598
. -
Кл.слова (ненормированные):
Adaptive Median Filter -- Contrast Limited Adaptive Histogram Equalization -- Elastic Maps -- Medical Image Processing -- Noise reduction -- Adaptive filtering -- Adaptive filters -- Computational methods -- Computer aided diagnosis -- Data visualization -- Equalizers -- Graphic methods -- Image analysis -- Information systems -- Information use -- Median filters -- Medical image processing -- Medical imaging -- Noise abatement -- Pathology -- Tissue -- Tissue regeneration -- Adaptive histogram equalization -- Adaptive median filter -- Computational tools -- Computer-aided systems -- Contrast Limited Adaptive Histogram Equalization (CLAHE) -- Electron scanning microscopies -- Histological images -- Segmentation algorithms -- Image segmentation
Аннотация: The emerging field of in cancer pathology (computational pathology) using histological images of biopsies is a computer aided diagnosis. This paper devoted to computational methods for assessing the indicators of the process of tissue regeneration with the release of nickel-mesh reticulated titanium implants with shape memory. Accordingly, in order to design such computer-aided system that is able to measure the implants size and growth rate, this paper presents a computational toolkit. Moreover, this toolkit used to analyze the dynamics of the process and highlight the internal geometric features of the experimental images (segmentation algorithms and visualization of spatial data). The computational tools for preprocessing visual data is the important concept solution to increase the contrast and brightness of the analyzed images based on the Contrast Limited Adaptive Histogram Equalization (CLAHE). Finally, the result of proposed technique shows that the increasing the accuracy of estimates for testing data through (15-20%). © 2019 Association for Computing Machinery.
Scopus,
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Держатели документа:
Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, Russian Federation
Dir. of Computer Center, University of Fallujah, Iraq
Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences, Krasnoyarsk, Russian Federation
Доп.точки доступа:
Hamad, Y.; Mohammed, O. K.J.; Simonov, K.
Кл.слова (ненормированные):
Adaptive Median Filter -- Contrast Limited Adaptive Histogram Equalization -- Elastic Maps -- Medical Image Processing -- Noise reduction -- Adaptive filtering -- Adaptive filters -- Computational methods -- Computer aided diagnosis -- Data visualization -- Equalizers -- Graphic methods -- Image analysis -- Information systems -- Information use -- Median filters -- Medical image processing -- Medical imaging -- Noise abatement -- Pathology -- Tissue -- Tissue regeneration -- Adaptive histogram equalization -- Adaptive median filter -- Computational tools -- Computer-aided systems -- Contrast Limited Adaptive Histogram Equalization (CLAHE) -- Electron scanning microscopies -- Histological images -- Segmentation algorithms -- Image segmentation
Аннотация: The emerging field of in cancer pathology (computational pathology) using histological images of biopsies is a computer aided diagnosis. This paper devoted to computational methods for assessing the indicators of the process of tissue regeneration with the release of nickel-mesh reticulated titanium implants with shape memory. Accordingly, in order to design such computer-aided system that is able to measure the implants size and growth rate, this paper presents a computational toolkit. Moreover, this toolkit used to analyze the dynamics of the process and highlight the internal geometric features of the experimental images (segmentation algorithms and visualization of spatial data). The computational tools for preprocessing visual data is the important concept solution to increase the contrast and brightness of the analyzed images based on the Contrast Limited Adaptive Histogram Equalization (CLAHE). Finally, the result of proposed technique shows that the increasing the accuracy of estimates for testing data through (15-20%). © 2019 Association for Computing Machinery.
Scopus,
Смотреть статью
Держатели документа:
Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, Russian Federation
Dir. of Computer Center, University of Fallujah, Iraq
Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences, Krasnoyarsk, Russian Federation
Доп.точки доступа:
Hamad, Y.; Mohammed, O. K.J.; Simonov, K.