[Text] : статья / T. Popova [et al.] // BMC Genomics. - 2008. - Vol. 9. - Ст. 91DOI 10.1186/1471-2164-9-91
. -
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Доп.точки доступа:
Popova, Tatiana; Попова Т.Г.; Mennerich, Detlev; Weith, Andreas; Quast, Karsten
Труды сотрудников ИВМ СО РАН
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Effect of RNA quality on transcript intensity levels in microarray analysis of human post-mortem brain tissues
Secret life of tiny blood vessels: Lactate, scaffold and beyond
/ V. Salmin [et al.] // (26 April 2017 through 28 April 2017 : Springer Verlag, 2017. - Vol. 10208 LNCS. - P591-601, DOI 10.1007/978-3-319-56148-6_53
. -
Кл.слова (ненормированные):
Angiogenesis in vitro -- Brain microvessel endothelial cells -- Form factor -- Fractal dimension -- Gelatin scaffold -- Bioinformatics -- Biomedical engineering -- Blood vessels -- Brain -- Cells -- Cytology -- Endothelial cells -- Fractal dimension -- Fractals -- Angiogenesis -- Bio-scaffolds -- Form factors -- In-vitro -- Microenvironments -- Microvessel -- Microvessels -- Primary culture -- Scaffolds (biology)
Аннотация: We studied the model of cerebral angiogenesis in vitro using lactate-releasing gelatin bioscaffolds and primary culture of brain endothelial cells. We found that development of microvessels from actively proliferating rat brain microvessels endothelial cells was greatly modified by the presence of lactate at the surface of the scaffold with different lactate-releasing ability. Fractal dimension of newly-established vessel loops allows precise characterizing the local microenvironment supporting cell growth on various types of gelatin scaffolds. © Springer International Publishing AG 2017.
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Держатели документа:
Research Institute of Molecular Medicine and Pathobiochemistry, Krasnoyarsk State Medical University, p. Zheleznyaka str., 1, Krasnoyarsk, Russian Federation
Institute of Computational Modelling of SB RAS, Akademgorodok, Krasnoyarsk, Russian Federation
Доп.точки доступа:
Salmin, V.; Morgun, A.; Khilazheva, E.; Pisareva, N.; Boitsova, E.; Lavrentiev, P.; Sadovsky, M.G.; Садовский, Михаил Георгиевич; Salmina, A.
Кл.слова (ненормированные):
Angiogenesis in vitro -- Brain microvessel endothelial cells -- Form factor -- Fractal dimension -- Gelatin scaffold -- Bioinformatics -- Biomedical engineering -- Blood vessels -- Brain -- Cells -- Cytology -- Endothelial cells -- Fractal dimension -- Fractals -- Angiogenesis -- Bio-scaffolds -- Form factors -- In-vitro -- Microenvironments -- Microvessel -- Microvessels -- Primary culture -- Scaffolds (biology)
Аннотация: We studied the model of cerebral angiogenesis in vitro using lactate-releasing gelatin bioscaffolds and primary culture of brain endothelial cells. We found that development of microvessels from actively proliferating rat brain microvessels endothelial cells was greatly modified by the presence of lactate at the surface of the scaffold with different lactate-releasing ability. Fractal dimension of newly-established vessel loops allows precise characterizing the local microenvironment supporting cell growth on various types of gelatin scaffolds. © Springer International Publishing AG 2017.
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Держатели документа:
Research Institute of Molecular Medicine and Pathobiochemistry, Krasnoyarsk State Medical University, p. Zheleznyaka str., 1, Krasnoyarsk, Russian Federation
Institute of Computational Modelling of SB RAS, Akademgorodok, Krasnoyarsk, Russian Federation
Доп.точки доступа:
Salmin, V.; Morgun, A.; Khilazheva, E.; Pisareva, N.; Boitsova, E.; Lavrentiev, P.; Sadovsky, M.G.; Садовский, Михаил Георгиевич; Salmina, A.
004.932
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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.
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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.