Email this article 
The Method of Decomposition of Biological Objects on MRI Images with a Similar Background
- Authors: Gridin V.N.1, Trufanov M.I.1, Yakhno N.N.1, Sinitsyn V.E.1
-
Affiliations:
- Design Information Technologies Center Russian Academy of Sciences
- Issue: No 2 (2024)
- Pages: 37-51
- Section: Information processing and data analysis
- URL: https://ogarev-online.ru/2071-8632/article/view/288000
- DOI: https://doi.org/10.14357/20718632240204
- EDN: https://elibrary.ru/MQQEAA
- ID: 288000
Cite item
Open Access
Access granted
Subscription Access
Abstract
The results of the development of a method for recognizing and measuring objects on MRI brain inventions based on the introduction of a mathematical description of the transition area between biological objects and the subsequent decomposition of the general image of the brain into separate biological objects are presented. A distinctive feature of the developed approach is the use of neural networks only to simplify the search for key features of a given biological object, whereas the main part of the method is implemented in the form of procedures for calculating signs of transitions between biological structures by introducing a mathematical function and then filtering false detected transitions.
About the authors
Vladimir N. Gridin
Design Information Technologies Center Russian Academy of Sciences
Author for correspondence.
Email: info@ditc.ras.ru
scientific director, Dr.Sci.Tech., professor
Russian Federation, OdintsovoMaxim I. Trufanov
Design Information Technologies Center Russian Academy of Sciences
Email: info@ditc.ras.ru
candidate of technical sciences, senior researcher, docent
Russian Federation, OdintsovoNikolay N. Yakhno
Design Information Technologies Center Russian Academy of Sciences
Email: info@ditc.ras.ru
chief researcher, Doctor of Medical Sciences, academician
Russian Federation, OdintsovoValentin E. Sinitsyn
Design Information Technologies Center Russian Academy of Sciences
Email: info@ditc.ras.ru
chief researcher, Doctor of Medical Sciences, professor
Russian Federation, OdintsovoReferences
- Gridin V.N., Perepelov V.A., Panishchev V.S., Truphanov M.I., Yakhno N.N. Algorithm for calculating hippocampal parameters in the diagnosis of alzheimer's disease / Biomedical radioelectronics. 2019. V. 22. N 7. P. 5-15.
- Gridin V.N., Truphanov M.I., Solodovnikov V.I., Pan- ishchev V.S., Sinitsyn V.E., Yakhno N.N. Automatic anal- ysis of quantitative characteristics of the hippocampus in magnetic resonance imagraphy of the brain for the diagnosis of possible alzheimer's disease (review of literature and results of own research) / Radiology-practics. 2017. N 6
- (66). P. 41-59.
- Fawzi A, Achuthan A, Belaton B. Brain Image Segmentation in Recent Years: A Narrative Review. Brain Sciences. 2021; 11(8):1055. https://doi.org/10.3390/brainsci11081055.
- Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Methods Programs Biomed. 2018 Apr; 157:69-84. doi: 10.1016/j.cmpb.2018.01.003.
- Fedde van der Lijn a b, Tom den Heijer c d, Monique M.B. Breteler c, Wiro J. Niessen. Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts/ NeuroImage Volume 43, Issue 4, December 2008, Pages 708-720.
- Benjamin Thyreau, Kazunori Sato, Hiroshi Fukuda, Yasuyuki Taki / Segmentation of the hippocampus by transfer- ring algorithmic knowledge for large cohort processing // Medical Image Analysis,Volume 43,2018,Pages 214-228.
- Lin, D., Rajinikanth, V., Lin, H. (2021). Hybrid Image Pro- cessing-Based Examination of 2D Brain MRI Slices to Detect Brain Tumor/Stroke Section: A Study. In: Priya, E., Rajinikanth, V. Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15- 6141-2_2.
- Hasnain Ali Shah, Faisal Saeed, Sangseok Yun, Jun-Hyun Park, Anand Paul, Jae-Mo Kang, "A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet", IEEE Access, vol.10, pp.65426-65438, 2022.
- K. Tartarotti Nepomuceno Duarte, M. Andrade Nascimento Moura, P. Sergio Martins and M. A. Garcia de Carvalho, "Brain Extraction in Multiple T1-weighted Magnetic Reso- nance Imaging slices using Digital Image Processing techniques," in IEEE Latin America Transactions, vol. 20, no. 5, pp. 831-838, May 2022, doi: 10.1109/TLA.2022.9693568.
- Automated Classification of Alzheimer's Disease Based on MRI Image Processing using Convolutional Neural Net- work (CNN) with AlexNet Architecture, Y N Fu'adah, I Wijayanto, N K C Pratiwi, F F Taliningsih, S Rizal and M. A.Pramudito. Journal of Physics: Conference Se- ries, Volume 1844, 2020 2nd International Conference on Science & Technology (2020 2nd ICoST) 28 November 2020, Yogyakarta, Indonesia.
- Yi, P., Jin, L., Xu, T., Wei, L. and Rui, G. (2021), Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey. Chinese J. Electron., 30: 793-814.
- MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hip- pocampal subfields segmentation. Baoqiang Ma, Yan Zhao, Yujing Yang et al / Computerized Medical Imaging and Graphics, Volume 86, December 2020, 101800.
- Zhenxi Zhang, Jie Li, Chunna Tian, Zhusi Zhong, Zhicheng Jiao, Xinbo Gao / Quality-driven deep active learning method for 3D brain MRI segmentation // Neurocomputing, Volume 446, 2021, Pages 106-117.
- Gridin V.N., Gazov A.I., Novikov I.A., Solodovnikov V.I., Trufanov M.I. / Nonscalable functions as models of the boundary of a biological object // High Availability Sys- tems. 2021. V. 17. N 4. P. 66-75.
- Dash, S., & Jena, U. Multi-resolution Laws’ Masks based texture classification. Journal of Applied Research and Technology, 2019, 15(6). https://doi.org/10.1016/j.jart.2017.07.005
- Bochkovskiy, A., Wang, C.-Y., & Mark Liao, H.-Y. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv. https://arxiv.org/pdf/2004.10934.pdf
- V.N. Gridin, M.I. Trufanov, V.I. Solodovnikov, V.S. Panishchev, V.E. Sinitsyn, N.N. Yakhno. Automatic analysis of the quantitative characteristics of the hippocampus in magnetic resonance imaging of the brain for the diagnosis of possible Alzheimer's disease (literature review and results of our own research) / Radiology - practice. No. 6 2017.S. 41-59.
- Chuan Luo, Wei Yang, Panling Huang, Jun Zhou. Over- view of Image Matching Based on ORB Algorithm. IOP Conf. Series: Journal of Physics: Conf. Series 1237 (2019), doi: 10.1088/1742-6596/1237/3/032020.
- Surface reconstruction from a sparse point cloud by enforcing visibility consistency and topology constraints Com- puter Vision and Image Understanding Volume 175, October 2018, Pages 52-71 https://doi.org/10.1016/j.cviu.2018.09.007.
- Detail Preserved Surface Reconstruction from Point Cloud Yang Zhou, Shuhan Shen and Zhanyi Hu Sensors 2019, 19, 1278; doi: 10.3390/s19061278.
- Yakhno N.N., Gridin V.N., Perepelov V.A., Sinitsyn V.E., Koberskaya N.N., Smirnov D.S., Solodovnikov V.I. 1, Trufanov M.I. / Hippocampal volume based on mrivolu- metric analysis in alzheimer disease patients // Russian electronic journal of radiology. 2020. V. 10. N 1. P. 50-58.
Supplementary files
