TOMOGRAPHIC AND X-RAY RECOGNITION SYSTEM FOR SEARCHING AND LOCALIZING PATHOLOGIES

A. I. Poliachenko

Abstract


The article proposed and built a tomographic and X-ray recognition system for the search and localization of pathologies. This system includes blocks: the entry of patient information, medical image processing, to establish a conclusion, to classify the pathologies identified, a database and a report. In the proposed system, the initial step is to obtain tomographic or X-ray images, which are then transferred to the patient information and medical image processing units. As a result of the introduction of information about the patient enters the database along with tomographic or x-ray images. The medical image processing unit includes 4 subsystems that cover the automatic processing of tomographic or X-ray images, as well as manual processing by a diagnostician. On the basis of the developed tomographic and X-ray recognition system for the search and localization of pathologies, it is possible to give an opinion about the disease automatically, or based on the judgments of the diagnostician.

Key words: recognition system, tomography and X-rays, pathology search and localization, diagnostician, database.


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References


Поляченко, А.І. (2018). Можливості методів пошуку та локалізації аномалій у томографічних та рентгенівських знімках для підвищення точності встановлення діагнозу. Наукові нотатки, 64,159-165.

Поляченко, А.І. (2019). Пошук контурів областей із певною патологічною структурою на МРТ знімках. Комп’ютерно-інтегровані технології: освіта, наука, виробництво, 34, 96-106.

Mustaqeem, A., Javed, A., & Fatima, T. (2012). An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. International Journal of Image, Graphics and Signal Processing, 4(10), 34-39.

Logeswari, T., & Karnan, M. (2010). An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map. International Journal of Computer Theory and Engineering, 2(4), p. 1793-8201.

Marr, D., Hildreth, E. (2007). Proceedings of the Royal Society of London. Biological Sciences.Series B, 1167, 187-217.

Kharrat, A. Benamrane, N., Ben, M. M., Abid, M. (2009). Detection of brain tumor in medical images. International Conference on Signals, Circuits and Systems, 1-6.

Bansal, S., Maini, R. (2013). Performance analysis of color based region split and merge and otsu’s thresholding techniques for brain tumor extraction. International Journal of Engineering Research and Applications, 3(4), 1640-1643.

Aslam, H. A., Ramashri, T., & Ahsan, M. I. A. (2013). A new approach to image segmentation for brain tumor detection using pillar K-means algorithm. Int J Adv Res Comput Commun Eng, 2(3), 1429-1436.

Кирсанова, А.В. (2004). Автоматизированная система диагностики новообразований головного мозга по магнитно-резонансным изображениям: дисс. ... канд. тех. наук: 05.11.17, С.-Петерб. гос. электротехн. ун-т (ЛЭТИ). Санкт-Петербург, 2010. 148 с.

References:

Poliachenko, A. I. (2018). Mozhlyvosti metodiv poshuku ta lokalizatsii anomalii u tomohrafichnykh ta renthenivskykh znimkakh dlia pidvyshchennia tochnosti vstanovlennia diahnozu [Possibilities of methods for searching and localizing anomalies in tomography and x-ray images to improve the accuracy of diagnosis]. Naukovi notatky [Science notes], 64,159-165 [in Ukrainian].

Poliachenko, A. I. (2019). Poshuk konturiv oblastei iz pevnoiu patolohichnoiu strukturoiu na MRT znimkakh [Search for the contours of areas with a specific pathological structure on MRI images]. Kompiuterno-intehrovani tekhnolohii: osvita, nauka, vyrobnytstvo [Computer-integrated technologies: education, science, production], 34, 96-106 [in Ukrainian].

Mustaqeem, A., Javed, A., & Fatima, T. (2012). An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. International Journal of Image, Graphics and Signal Processing, 4(10), 34-39.

Logeswari, T., & Karnan, M. (2010). An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map. International Journal of Computer Theory and Engineering, 2(4), p. 1793-8201.

Marr, D., Hildreth, E. (2007). Proceedings of the Royal Society of London. Biological Sciences.Series B, 1167, 187-217.

Kharrat, A. Benamrane, N., Ben, M. M., Abid, M. (2009). Detection of brain tumor in medical images. International Conference on Signals, Circuits and Systems, 1-6.

Bansal, S., Maini, R. (2013). Performance analysis of color based region split and merge and otsu’s thresholding techniques for brain tumor extraction. International Journal of Engineering Research and Applications, 3(4), 1640-1643.

Aslam, H. A., Ramashri, T., & Ahsan, M. I. A. (2013). A new approach to image segmentation for brain tumor detection using pillar K-means algorithm. Int J Adv Res Comput Commun Eng, 2(3), 1429-1436.

Kirsanova, A.V. (2004). Avtomatizirovannaja sistema diagnostiki novoobrazovanij golovnogo mozga po magnitno-rezonansnym izobrazhenijam [Automated system for diagnosis of brain tumors by magnetic resonance imaging] (PhD Thesis), St. Petersburg [in Russian].




DOI: https://doi.org/10.26886/2414-634X.6(33)2019.5

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