Sharpness improvement of magnetic resonance images using a guided-subsumed unsharp mask filter

• Autorzy: Al-Abaji Manar , Al-Ameen Zohair

Identyfikatory

DOI

10.35784/acs-2024-47

• Języki publikacji: EN

Abstrakty

EN

Magnetic resonance imaging (MRI) is a key method for imaging human tissues and organs. The accuracy of medical diagnosis is greatly affected by the quality of MRI images. Sometimes, MRI images are obtained blurry due to various inevitable constraints related to the imaging equipment, which affects the detection of important features in the image. Several sharpening methods were introduced, but not all were successful in this task, as artifacts may be introduced, contrast may be changed, and high complexity may be involved. Thus, this paper introduces a guided-subsumed unsharp mask filter (GSUM) to improve the sharpness of MRI images. The GSUM utilizes an improved guided filter instead of the low-pass Gaussian filter and a dynamic sharpening parameter. The improved guided filter employs a hybrid procedure instead of the mean filter in the smoothing process and relies on an adaptive regularization parameter. The applied modifications eliminated the overshooting and halo effects of the original unsharp masking and the guided filter, resulting in better-quality images. The GSUM was tested with real-blurry MRI images, evaluated using three no-reference metrics, and compared with six other algorithms. The metric scores indicate that the proposed filter can surpass existing methods, as it produced better results with average readings of 24.2074 in PIQE, 0.6878 in BLUR, and 5.7944 in FISH. It also scored a fast computation time, averaging 0.3384 seconds.

Słowa kluczowe

EN

unsharp mask; guided filter; image sharpening; MRI images

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 4
• Strony: 192--210
• Opis fizyczny: Bibliogr. 36 poz., fig., tab.

Twórcy

autor

Al-Abaji Manar

• University of Mosul, College of Education for Pure Science, Department of Computer Science

• https://orcid.org/0000-0001-9251-8920

autor

Al-Ameen Zohair

• University of Mosul, University of Mosul Presidency, Computer Center, ICT Research Unit

• https://orcid.org/0000-0003-3630-2134

Bibliografia

• [1] Al-Ameen, Z., Al-Healy, M. A., & Hazim, R. A. (2020). Anisotropic diffusion-based unsharp masking for sharpness improvement in digital images. Journal of Soft Computing and Decision Support Systems, 7(1), 7-12.
• [2] Al-Ameen, Z., Muttar, A., & Al-Badrani, G. (2019). Improving the sharpness of digital image using an amended unsharp mask filter. International Journal of Image, Graphics and Signal Processing, 11(3), 1-9. https://doi.org/10.5815/ijigsp.2019.03.01
• [3] Bogdan, V., Bonchiş, C., & Orhei, C. (2024). An image sharpening technique based on dilated filters and 2D-DWT image fusion. 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) (pp. 591-598). SciTePress. https://doi.org/10.5220/0012416600003660
• [4] Calder, J., Mansouri, A., & Yezzi, A. (2010). Image sharpening via Sobolev gradient flows. SIAM Journal on Imaging Sciences, 3(4), 981-1014. https://doi.org/10.1137/090771260
• [5] Cao, G., Zhao, Y., Ni, R., & Kot, A. C. (2011). Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Processing Letters, 18(10), 603-606. https://doi.org/10.1109/LSP.2011.2164791
• [6] Chen, T. J. (2019). An adaptive image sharpening scheme. Multi Conference on Computer Science and Information Systems, MCCSIS 2019 - Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2019, Game and Entertainment Technologies 2019 and Computer Graphics, Visualization, Comp (pp. 396-400). International Association for development of the information society. https://doi.org/10.33965/g2019_201906c056
• [7] Crete, F., Dolmiere, T., Ladret, P., & Nicolas, M. (2007). The blur effect: perception and estimation with a new no-reference perceptual blur metric. Human Vision and Electronic Imaging, 6492. https://doi.org/10.1117/12.702790
• [8] Demir, Y., & Kaplan, N. H. (2023). Low-light image enhancement based on sharpening-smoothing image filter. Digital Signal Processing, 138, 104054. https://doi.org/10.1016/j.dsp.2023.104054
• [9] Deng, G. (2010). A generalized unsharp masking algorithm. IEEE Transactions on Image Processing, 20(5), 1249-1261. https://doi.org/10.1109/TIP.2010.2092441
• [10] Deng, G., Galetto, F., Alnasrawi, M., & Waheed, W. (2021). A guided edge-aware smoothing-sharpening filter based on patch interpolation model and generalized gamma distribution. IEEE Open Journal of Signal Processing, 2, 119-135. https://doi.org/10.1109/OJSP.2021.3063076
• [11] Edla, D. R., Simi, V. R., & Joseph, J. (2022). A noise-robust and overshoot-free alternative to unsharp masking for enhancing the acuity of MR images. Journal of Digital Imaging, 35, 1041-1060. https://doi.org/10.1007/s10278-022-00585-z
• [12] Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing, 3rd Edition. Pearson Prentice Hall.
• [13] Gui, Z., & Liu, Y. (2011). An image sharpening algorithm based on fuzzy logic. Optik, 122(8), 697-702. https://doi.org/10.1016/j.ijleo.2010.05.010
• [14] Habee, N. J. (2021). Performance enhancement of medical image fusion based on DWT and sharpening Wiener filter. Jordanian Journal of Computers and Information Technology, 7(2), 118-129. https://doi.org/10.5455/jjcit.71-1610049522
• [15] He, K., Sun, J., & Tang, X. (2013). Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1397-1409. https://doi.org/10.1109/TPAMI.2012.213
• [16] Holder, R. P., & Tapamo, J. R. (2017). Improved gradient local ternary patterns for facial expression recognition. EURASIP Journal on Image and Video Processing, 2017, 42. https://doi.org/10.1186/s13640-017-0190-5
• [17] Huang, Q. (2021). An image sharpness enhancement algorithm based on green function. Traitement Du Signal, 38(2), 513-519. https://doi.org/10.18280/ts.380231
• [18] Joseph, J., Anoop, B. N., & Williams, J. (2019). A modified unsharp masking with adaptive threshold and objectively defined amount based on saturation constraints. Multimedia Tools and Applications, 78, 11073-11089. https://doi.org/10.1007/s11042-018-6682-1
• [19] Kheradmand, A., & Milanfar, P. (2015). Non-linear structure-aware image sharpening with difference of smoothing operators. Frontiers in ICT, 2, 22. https://doi.org/10.3389/fict.2015.00022
• [20] Kim, S., & Allebach, J. P. (2005). Optimal unsharp mask for image sharpening and noise removal. Journal of Electronic Imaging, 14(2), 023005. https://doi.org/10.1117/1.1924510
• [21] Li, L., Wu, D., Wu, J., Li, H., Lin, W., & Kot, A. C. (2016). Image sharpness assessment by sparse representation. IEEE Transactions on Multimedia, 18(6), 1085-1097. https://doi.org/10.1109/TMM.2016.2545398
• [22] Li, P., Wang, H., Yu, M., & Li, Y. (2021). Overview of image smoothing algorithms. 2nd International Conference on Computer Information and Big Data (012024). Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1883/1/012024
• [23] Ngo, D., Lee, S., & Kang, B. (2020). Nonlinear unsharp masking algorithm. 2020 International Conference on Electronics, Information, and Communication (ICEIC) (pp. 1-6). IEEE. https://doi.org/10.1109/ICEIC49074.2020.9051376
• [24] Osher, S., & Rudin, L. I. (1990). Feature-oriented image enhancement using shock filters. SIAM Journal on Numerical Analysis, 27(4), 919-940. https://doi.org/10.1137/0727053
• [25] Jeevakala, S., & Therese, A. B. (2018). Sharpening enhancement technique for MR images to enhance the segmentation. Biomedical Signal Processing and Control, 41, 21-30. https://doi.org/10.1016/j.bspc.2017.11.007
• [26] Sadah, Y. A., Al-Najdawi, N., & Tedmori, S. (2013). Exploiting hybrid methods for enhancing digital X-ray images. International Arab Journal of Information Technology, 10(1), 28-35.
• [27] Sheppard, A. P., Sok, R. M., & Averdunk, H. (2004). Techniques for image enhancement and segmentation of tomographic images of porous materials. Physica A: Statistical Mechanics and Its Applications, 339(1-2), 145-151. https://doi.org/10.1016/j.physa.2004.03.057
• [28] Shi, Z., Chen, Y., Gavves, E., Mettes, P., & Snoek, C. G. M. (2021). Unsharp mask guided filtering. IEEE Transactions on Image Processing, 30, 7472-7485. https://doi.org/10.1109/TIP.2021.3106812
• [29] Simi, V. R., Edla, D. R., & Joseph, J. (2023). An inverse mathematical technique for improving the sharpness of magnetic resonance images. Journal of Ambient Intelligence and Humanized Computing, 14, 2061-2075. https://doi.org/10.1007/s12652-021-03416-1
• [30] Singh, U., & Choubey, M. K. (2021). A review: image enhancement on MRI images. 2021 5th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1-6). IEEE. https://doi.org/10.1109/ISCON52037.2021.9702464
• [31] Venkatanath, N., Praneeth, D., Maruthi Chandrasekhar, Bh., Channappayya, S. S., & Medasani, S. S. (2015). Blind image quality evaluation using perception based features. 2015 Twenty First National Conference on Communications (NCC) (pp. 1-6). IEEE. https://doi.org/10.1109/NCC.2015.7084843
• [32] Vin Toh, K. K., & Mat Isa, N. A. (2011). Locally adaptive bilateral clustering for image deblurring and sharpness enhancement. IEEE Transactions on Consumer Electronics, 57(3), 1227-1235. https://doi.org/10.1109/TCE.2011.6018878
• [33] Vu, P. V., & Chandler, D. M. (2012). A fast wavelet-based algorithm for global and local image sharpness estimation. IEEE Signal Processing Letters, 19(7), 423-426. https://doi.org/10.1109/lsp.2012.2199980
• [34] Yang, C.-C. (2014). Finest image sharpening by use of the modified mask filter dealing with highest spatial frequencies. Optik - International Journal for Light and Electron Optics, 125(8), 1942-1944. https://doi.org/10.1016/j.ijleo.2013.09.070
• [35] Zafeiridis, P., Papamarkos, N., Goumas, S., & Seimenis, I. (2016). A new sharpening technique for medical images using wavelets and image fusion. Journal of Engineering Science and Technology Review, 9(3), 187-200. https://doi.org/10.25103/jestr.093.27
• [36] Zhang, R., & Wu, J. (2023). A bidirectional guided filter used for RGB-D maps. IEEE Transactions on Instrumentation and Measurement, 72, 5009714. https://doi.org/10.1109/TIM.2023.3256467

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).