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V.N. Gridin, N.N. Yakhno, V.E. Sinitsyn, V.A. Perepelov, M.I. Trufanov, V.A. Vinogradov Algorithm for searching the hippocampus on a series of magnetic resonance images of the brain in the diagnosis of Alzheimer's disease |
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Abstract.The article deals with the development of the algorithm for detecting the hippocampus, which is necessary to achieve the goal the task. The algorithm is based on the mathematical foundations of decisionmaking on the basis of fuzzy logic when using as the initial data the results of preliminary image processing and the results of object recognition based on their reference description. The novelty of the algorithm is the use of procedures for selecting key images for calculating the parameters of preliminary image processing, using the spectral characteristics of individual regions of the brain on a two-dimensional image in the sagittal projection to clarify the coordinates of the hippocampus, deciding whether a hippocampus is based on fuzzy logic (taking into account direct and indirect signs). These innovations provide software implementation of the algorithm for evaluating the characteristics of the hippocampus in an automatic mode, which will positively affect the quality and speed of diagnosis of the disease. Keywords: recognition, hippocampus, computer science, image processing, Alzheimer`s desease PP. 23-32. DOI 10.14357/20718632180403 References 1. Yakhno N.N., Zakharov V.V., Lokshina A.B., Koberskaya N.N.: Dementia. A guide for doctors. - Moscow, 2011. – 243. 2. Afshan N., Qureshi S., Syed Mujtiba Hussain, Comparative study of tumor detection algorithms, Medical Imaging, 2014 International Conference on, pp. 251-256, 2014. 3. Hunnur S., Raut A., Kulkarni S., "Implementation of image processing for detection of brain tumors", Computing Methodologies and Communication (ICCMC) 2017 International Conference on, pp. 717-722, 2017. 4. Kapoor L., Thakur S., "A survey on brain tumor detection using image processing techniques", Cloud Computing Data Science & Engineering - Confluence 2017 7th International Conference on, pp. 582-585, 2017. 5. Udupa J., Saha P., "Fuzzy Connectedness and Image Segmentation", Proceedings of the IEEE, vol. 91, 2003. 6. Gridin V.N. Automatic analysis of the quantitative characteristics of the hippocampus in the magnetic resonance imaging of the brain for the diagnosis of possible Alzheimer's disease / V.N. Gridin, M. I. Trufanov, V.I Solodovnikov, V.S Panischev, V.E. Sinitsyn, N. N. Yakhno // Radiology-Practice, 2017. - №6. - from. 42-67. 7. Chupin M, Gerardin E, Cuingnet R. et al. Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI. Hippocampus 2009; 19: 579-87. 8. A. Singh, K. Singh, "A Study of Image Segmentation Algorithms for Different Types of Images", International Journal of Computer Science Issues, vol. 7, Issue 5, pp 414-417, 2010. 9. Mustaqeem A., Ali Javed, Fatima T., "An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation", I.J. Image, Graphics and Signal Processing, vol. 10, no. 5, pp 34-39, 2012. 10. Mankikar S.S., "A Novel Hybrid Approach Using Kmeans Clustering and Threshold Filter For Brain Tumor Detection", International Journal of Computer Trends and Technology, vol. 4, no.3, pp 206-209, 2013. 11. S.M. Ali, Loay Kadom Abood, and Rabab Saadoon Abdoon, "Brain Tumor Extraction in MRI Images using Clustering and Morphological Operations Techniques", International Journal of Geographical Information System Application. 12. Cuingnet R., Gerardin E., Tessieras J. et al. Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database // NeuroImage. 2011. №56. p. 766-781.
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