Denoising and Analysis Methods of Computer Tomography Results of Lung Diagnostics for Use in Neural Network Technology

• Autorzy: Slavova Oleksandra , Lebid Solomiya
• Języki publikacji: EN

Abstrakty

EN

Any type of biomedical screening emerges large amounts of data. As a rule, these data are unprocessed and might cause problems during the analysis and interpretation. It can be explained with inaccuracies and artifacts, which distort all the data. That is why it is crucial to make sure that the biomedical information under analysis was of high quality to omit to receive possibly wrong results or incorrect diagnosis. Receiving qualitative and trustworthy biomedical data is a necessary condition for high-quality data assessment and diagnostics. Neural networks as a computing system in data analysis provide recognizable and clear datasets. Without such data, it becomes extremely difficult to make a diagnosis, predict the course of the disease, and treatment result. The object of this research was to define, describe, and test a new approach to the analysis and preprocessing of the biomedical images, based on segmentation. Also, it was summarized different metrics for assessing image quality depending on the purpose of research. Based on the collected data, the advantages and disadvantages of each of the methods were identified. The proposed method of analysis and noise reduction was applied to the results of computed tomography lungs screening. Based on the appropriate evaluation metrics, the obtained results were evaluated quantitatively and qualitatively. As a result, the expediency of the proposed algorithm application was proven.

Słowa kluczowe

EN

computed tomography; CT scans analysis; convolutional neural network; image clustering; image denoising; k-means clustering

• Wydawca: Polish Academy of Sciences, Branch in Lublin
• Czasopismo: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
• Rocznik: 2020
• Tom: Vol. 9, No 1
• Strony: 19--24
• Opis fizyczny: Bibliogr 9 poz., rys., tab., wz.

Twórcy

autor

Slavova Oleksandra

• Department of Electronics and Information Technology, Institute of Telecommunications, Radioelectronics and Electronic Engineering Lviv Polytechnic National University, 12 Bandera street, Lviv, 79013

autor

Lebid Solomiya

• Department of Electronics and Information Technology, Institute of Telecommunications, Radioelectronics and Electronic Engineering Lviv Polytechnic National University, 12 Bandera street, Lviv, 79013

Bibliografia

• 1. Kachelrieß, M. Kalender, W. A. 2005. Presampling, algorithm factors, and noise: Considerations for CT in particular and for medical imaging in general, Medical Physics, 32 (5), pp. 1321–1334.
• 2. P. Sprawls. Sep. 1992. AAPM Tutorial. CT Image detaіl and noise. Radiographics, Vol.12, no. 5, Pp. 1041-1046.
• 3. Attivissimo, F. Cavone, G. Lanzolla, A. M. L. and Spadavecchia, M.. 2010. A technique to improve the image quality in computer tomography. IEEE Trans. Intrum. Meas., vol. 59, no. 5, pp. 1251-1257.
• 4. Chen, Y.C. Hong, D. Wu, C.W. Mupparapu, M. 2019. The Use of Deep Convolutional Neural Networks in Biomedical Imaging: A Review, pp. 3-10.
• 5. Bejnordi, E.B., Mullooly, M., Pfeiffer, R.M., Fan, S., Vacek, P.M., Weaver, D.L. et al. 2018. Using deep convolutional neural networks to identify and classify tumor associated stroma in diagnostic breast biopsies. Mod Pathol; 31, pp. 1502-1512.
• 6. Gao M, Bagci U, Lu L, Wu A, Buty M, Shin HC et al. 2018. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis: 6, pp. 1-6.
• 7. Guo, Y. Ashour, A. S.. 2019. Neutrosophic sets in dermoscopic medical image segmentation, pp. 229-243.
• 8. Ng, H.P. Ong, S.H. Foong, K.W.C. Goh, P.S. Nowinski, W.L. 2006. Medical image segmentation using k-means clustering and improved watershed algorithm. Image Analys. And Interpret, IEEE Southwest Sympos, pp. 61-65.
• 9. Sara, U., Akter, M. and Uddin, M.S. 2019 “Image Quality Assessment through FSIM, SSIM, MSE and PSNR— A Comparative Study», Journal of Computer and Communications, pp. 8-18.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).