Qorin boʻshligʻi tasvirlarida buyrak tomirlarini ajratib olish algoritmi

Axmad Xashimov

Авторы

Axmad Xashimov Qo'qon universiteti

Ключевые слова:

медицинская визуализация, обработка изображений, сегментация

Аннотация

Статья посвящена алгоритмам распознавания почечных вен на изображениях сосудов, полученных на основе изображений брюшной полости человека. В статье предлагается алгоритм распознавания вен по цвету и толщине на контрастных изображениях.

Библиографические ссылки

Anjamrooz SH, Azari H, Abedinzadeh M. Abnormal patterns of the renal veins. Anat Cell Biol. 2012 Mar;45(1):57-61.

Arévalo Pérez J, Gragera Torres F, Marín Toribio A, Koren Fernández L, Hayoun C, Daimiel Naranjo I. Angio CT assessment of anatomical variants in renal vasculature: its importance in the living donor. Insights Imaging. 2013 Apr;4(2):199-211.

Ильясова, Н.Ю. Информационные технологии анализа изображений в задачах медицинской диагностики – М.: Радио и связь, 2012. – 424 с.

Ильясова, Н.Ю. Методы и алгоритмы оценивания геометрических параметров диагностических изображений: дис. ... канд. техн. наук: 05.13.18 – Самара: СГАУ, 1997. – 155 с.

Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3- 319-24574-4_28

Mat Said, Khairul Anuar & Jambek, Asral & Sulaiman, Nasri. (2016). A study of image processing using morphological opening and closing processes. International Journal of Control Theory and Applications. 9. 15-21.

S. Radjabov, A. Khashimov, E. Urinov and S. Kakharov, Neural networks for medical image segmentation,2021 International Conference on Information Science and Communications Technologies (ICISCT), 2021, pp. 1-4, doi: 10.1109/ICISCT52966.2021.9670147.

Fazilov, S., Radjabov, S., Atakhanov, M., Khashimov, A. Access Control System Based on Facial Image Analysis. THE 1ST INTERNATIONAL CONFERENCE ON PROBLEMS AND PERSPECTIVES OF MODERN SCIENCE: ICPPMS-2021 10–11 June 2021. Tashkent, Uzbekistan. https://doi.org/10.1063/5.0089655

O. Ronneberger, P. Fischer, T. Brox, U-net, Convolutional networks for biomedical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015, pp. 234–241

Sh. Kh. Fazilov, N. M. Mirzaev, S. S. Radjabov, , G. R. Mirzaeva. Hybrid algorithms of the person identification by face image. Journal of Physics: Conference Series, 2019, 1333(3), 032016

Fazilov S., Urinov E., Kakharov S. and Khashimov A. Improving image contrast: Challenges and solutions 2021 International Conference on Information Science and Communications Technologies (ICISCT), 2021, pp. 1-5, doi: 10.1109/ICISCT52966.2021.9670106.

Khamdamov R.Kh., Saliev E.A., Mirzaev N.M., Ibragimova S.N. Segmentation of colour image using fuzzy sets concept // Journal of Physics: Conference Series. 1333 (2019) 032035 doi:10.1088/1742- 6596/1333/3/032035

Fazilov, S.K., Mirzaev, O.N., Kakharov, S.S. Building a Local Classifier for Component-Based Face Recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, 13741 LNCS, страницы 177–187

Opanasenko, V.M., Fazilov, S., Radjabov, S.S., Kakharov, S. Multilevel Face Recognition System. Cybernetics and Systems Analysis., 2024, 60(1), страницы 146–151

Buriboyev A., Khashimov A., Abduvaitov A., Jeon H.S., CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm. Sensors 2024, 24, 7703. https://doi.org/10.3390/s24237703