КРИТЕРИИ ОЦЕНКИ КАЧЕСТВА МЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ, ПОЛУЧЕННЫХ НА МУЛЬТИСПИРАЛЬНОМ КОМПЬЮТЕРНОМ ТОМОГРАФЕ
Article Sidebar
Main Article Content
Аннотация:
Оценка качества изображений, особенно медицинских изображений, полученных с помощью мультиспирального компьютерного томографа чрезвычайно важна в области медицинской визуализации. На качество медицинского изображения влияют различные факторы, в том числе характеристики устройства медицинской визуализации и используемый протокол визуализации. Кроме того, наличие шумов, артефактов и других факторов, снижающих качество изображения, может существенно повлиять на общее качество и диагностическую ценность получаемых изображений.
Article Details
Как цитировать:
Библиографические ссылки:
M. Narzillo, A. Bakhtiyor, K. Shukrullo, O. Bakhodirjon and A. Gulbahor, "Peculiarities of face detection and recognition," 2021 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2021, pp. 1-5, doi: 10.1109/ICISCT52966.2021.9670086.
N. S. Mamatov, B. A. Abdukadirov, A. N. Samijonov and B. N. Samijonov, "Method for false attack detection in face identification system," 2021 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2021, pp. 1-4, doi: 10.1109/ICISCT52966.2021.9670153.
Narzillo, M., Abdurashid, S., Nilufar, N., Musokhon, D., & Erkin, R. (2020). Definition of line formula on images. Journal of Physics: Conference Series, 1441(1), 012150. https://doi.org/10.1088/1742-6596/1441/1/012150
Niyozmatova, N. A., Mamatov, N., Samijonov, A., Abdukadirov, B., & Abdullayeva, B. M. (2020). Algorithm for determining the coefficients of the interpolation polynomial of Newton with separated differences. IOP Conference Series: Materials Science and Engineering, 862(4), 042019. https://doi.org/10.1088/1757-899X/862/4/042019
Mamatov, N. S., Niyozmatova, N. A., Jalelova, M. M., Samijonov, A. N., and Tojiboyeva, Sh. X., “Methods for improving contrast of agricultural images,” E3S Web Conf., vol. 401, p. 4020, 2023. DOI: 10.1051/e3sconf/202340104020
Wang, Zhou & Bovik, Alan & Sheikh, Hamid & Simoncelli, Eero. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. Image Processing, IEEE Transactions on. 13. 600 - 612. 10.1109/TIP.2003.819861.
M. Gupta, H. Taneja, and L. Chand, “Performance enhancement and analysis of filters in ultrasound image denoising,” Procedia Computer Science, vol. 132, pp. 643–652, 2018.
B. Goyall, A. Dogra1, S. Agrawal1, and B. S. Sohi, “Noise issues prevailing in various types of medical images,” Biomedical & Pharmacology Journal, vol. 11, pp. 1227-1237, September 2018.
M. H. Ali, “MRI medical image denoising by fundamental filters,” SCIREA Journal of Computer, vol. 2, pp. 12-26, 2017.
Priyanka Kamboj and Varsha Rani, “A Brief Study of Various Noise Model and filtering Techniques,” Journal of Global Research in Computer Science, Volume 4, No 4, pp.166-171, April 2013.
Thanh, Dang & Prasath, Surya & Le Minh, Hieu. (2019). A Review on CT and X-Ray Images Denoising Methods. Informatica. 43. 151-159. 10.31449/inf.v43i2.2179.
J. S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 2, pp. 165-168, March 1980.
Perumal, B., Sindhiya, R., y Pallikonda, M. (2021). Extermination methods of image noises: a review. 3C Tecnología. Glosas de innovación aplicadas a la pyme, Edición Especial, (noviembre, 2021), 243-259. https:// doi.org/10.17993/3ctecno.2021.specialissue8.243-259
Nik, M.M.Pura & Se, S.V.Hal. (2018). A Review Paper: Study of Various Types of Noises in Digital Images. International Journal of Engineering Trends and Technology. 57. 40-43. 10.14445/22315381/IJETT-V57P208.
Bindhya, P & Jegan, Chitra & Raj, V. (2020). A Review on Methods of Enhancement And Denoising in Retinal Fundus Images. INTERNATIONAL JOURNAL OF COMPUTER SCIENCES AND ENGINEERING. 8. 1-9. 10.26438/ijcse/v8i12.19.
Muna Khalid Jasim, RehanHamdullah Najm, Emran Hassn Kanan, Hamza Esam Alfaar, Mohammed Otair (2019). Image Noise Removal Techniques: A Comparative Analysis. International Journal of Science and Applied Information Technology. 10.30534/ijsait/2019/01862019
Bharati, Subrato & Khan, Tanvir & Podder, Prajoy & Hung, Nguyen. (2020). A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications. 10.1007/978-3-030-5588_3.
Maity, Alenrex & Chatterjee, Rishav. (2018). Impulsive Noise in Images: A Brief Review. Computer Vision Graphics and Image Processing. Vol 4. 6-15. 10.19101/TIPCV.2017.39025.
Garg, Gaurav & Juneja, Mamta. (2019). A survey of denoising techniques for multi-parametric prostate MRI. Multimedia Tools and Applications. 78. 10.1007/s11042-018-6487-2.
Bhonsle D, C.V., Sinha GR: ‘Medical image denoising using bilateral filter’, Int J Image Gr Signal Process 4, 2012, pp. 36–43
Uk, Ijeacs. (2017). Performance Assessment of Several Filters for Removing Salt and Pepper Noise, Gaussian Noise, Rayleigh Noise and Uniform Noise. International Journal of Engineering and Applied Computer Science (IJEACS). 02. 176-180. 10.24032/ijeacs/0206/01.
Ikhsan, Mohammad. (2021). Comparative Analysis of Different Algorithms for Image Denoising. 10.13140/RG.2.2.29939.14883.
Perumal, B., Sindhiya, R., y Pallikonda, M. (2021). Extermination methods of image noises: a review. 3C Tecnología. Glosas de innovación aplicadas a la pyme, Edición Especial, (noviembre, 2021), 243-259. https://doi.org/10.17993/3ctecno.2021.specialissue8.243-259
Kaur, Gurjinder & Garg, Meenu & Gupta, Sheifali & Gupta, Rupesh. (2021). Denoising of images using Thresholding Based on Wavelet Transform Technique. IOP Conference Series: Materials Science and Engineering. 1022. 012031. 10.1088/1757-899X/1022/1/012031.