[Текст] : учебное пособие / В.Е. Зализняк, Г.И. Щепановская. - Красноярск : Сибирский федеральный университет, 2011. - 120 с. - Библиогр.: с. 173. - ISBN 978-5-7638-2498-8 : Б. ц.
Аннотация: Изложены методы решения задач численного анализа, приведены краткое руководство по программированию в среде MATLAB и задания для практических занятий. Предназначено для студентов высших учебных заведений, обучающихся по специальности (направлению) подготовки ВПО 010501 (010500.62) «Прикладная математика и информатика» (ОПД.Ф.09 – Численные методы).
Доп.точки доступа:
Щепановская, Галина Ивановна; Shchepanovskaya G.I.
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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).
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.
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.
Identification of the elastic modules of a fibrous composite by solving inverse problems
/ I. E. Petrakov, V. M. Sadovskii // AIP Conference Proceedings : American Institute of Physics Inc., 2019. - Vol. 2164: 11th International Conference for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2019 (20 June 2019 through 25 June 2019, ) Conference code: 153460. - Ст. 090004, DOI 10.1063/1.5130834
. -
Аннотация: Generalized rheological method is used to construct constitutive equations of fiber composite materials with the fibers having high tensile stiffness and low stiffness upon compression. Polymer-based composite reinforced by thin carbon fibers, used in the aerospace industry, was chosen as the material for research. The method for determining the effective moduli of elasticity, based on the analysis of the bending state of a thin rod, was implemented to check the material's moduli. Photos of the bent rod with cantilever bending were processed to obtain a plane projection, and then digitized using computer programs. The deflection data along the rod length was used further in solving the inverse problem of determining flexural stiffness (it was considered constant) using the least squares method. By means of Matlab functions, the problem of minimizing the standard deviation of the calculated deflection from the digitized one was solved on a discrete system of points along the axis of the rod. The deflection was computed by the finite difference method based on the generalized equation of the Euler elastic line which takes into account the flexural stiffness, deformation along the rod, Timoshenko effect (the effect of shear deformation) and Cosserat effect (the effect of independent turns of the reinforcing fibers relative to the matrix). Young's modulus upon tension was measured experimentally using standard technique. Young's modulus upon compression was calculated using the value of flexural stiffness corresponding to the best approximation of calculated deflection to the digitized deflection. As a result, it was shown that the ratio of moduli for the material under consideration is in the range of 50-60% and, if the calculation of flexural stiffness is made with Young's modulus under tension, then it is possible to obtain a relative error in the deflection up to 30% with the increased thickness of the rod. © 2019 Author(s).
Scopus,
Смотреть статью
Держатели документа:
Institute of Computational Modeling SB RAS, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation
Доп.точки доступа:
Petrakov, I. E.; Sadovskii, V. M.
Аннотация: Generalized rheological method is used to construct constitutive equations of fiber composite materials with the fibers having high tensile stiffness and low stiffness upon compression. Polymer-based composite reinforced by thin carbon fibers, used in the aerospace industry, was chosen as the material for research. The method for determining the effective moduli of elasticity, based on the analysis of the bending state of a thin rod, was implemented to check the material's moduli. Photos of the bent rod with cantilever bending were processed to obtain a plane projection, and then digitized using computer programs. The deflection data along the rod length was used further in solving the inverse problem of determining flexural stiffness (it was considered constant) using the least squares method. By means of Matlab functions, the problem of minimizing the standard deviation of the calculated deflection from the digitized one was solved on a discrete system of points along the axis of the rod. The deflection was computed by the finite difference method based on the generalized equation of the Euler elastic line which takes into account the flexural stiffness, deformation along the rod, Timoshenko effect (the effect of shear deformation) and Cosserat effect (the effect of independent turns of the reinforcing fibers relative to the matrix). Young's modulus upon tension was measured experimentally using standard technique. Young's modulus upon compression was calculated using the value of flexural stiffness corresponding to the best approximation of calculated deflection to the digitized deflection. As a result, it was shown that the ratio of moduli for the material under consideration is in the range of 50-60% and, if the calculation of flexural stiffness is made with Young's modulus under tension, then it is possible to obtain a relative error in the deflection up to 30% with the increased thickness of the rod. © 2019 Author(s).
Scopus,
Смотреть статью
Держатели документа:
Institute of Computational Modeling SB RAS, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation
Доп.точки доступа:
Petrakov, I. E.; Sadovskii, V. M.