SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER
Manar AL-ABAJI
University of Mosul, College of Education for Pure Science, Department of Computer Science (Iraq)
https://orcid.org/0000-0001-9251-8920
Zohair AL-AMEEN
qizohair@uomosul.edu.iqUniversity of Mosul, University of Mosul Presidency, Computer Center, ICT Research Unit (Iraq)
https://orcid.org/0000-0003-3630-2134
Abstract
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.
Keywords:
Unsharp mask, Guided filter, Image sharpening, MRI imagesReferences
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.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing, 3rd Edition. Pearson Prentice Hall.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Authors
Manar AL-ABAJIUniversity of Mosul, College of Education for Pure Science, Department of Computer Science Iraq
https://orcid.org/0000-0001-9251-8920
Authors
Zohair AL-AMEENqizohair@uomosul.edu.iq
University of Mosul, University of Mosul Presidency, Computer Center, ICT Research Unit Iraq
https://orcid.org/0000-0003-3630-2134
Statistics
Abstract views: 69PDF downloads: 15
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Zaid ALSAYGH, Zohair AL-AMEEN, CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM , Applied Computer Science: Vol. 18 No. 2 (2022)
Similar Articles
- Wawan GUNAWAN, FUZZY REGION MERGING WITH HIERARCHICAL CLUSTERING TO FIND OPTIMAL INITIALIZATION OF FUZZY REGION IN IMAGE SEGMENTATION , Applied Computer Science: Vol. 20 No. 4 (2024)
- Puppala Praneeth, Majety Sathvika, Vivek Kommareddy, Madala Sarath, Saran Mallela, Koneru Suvarna Vani, Prasun Chkrabarti, CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Edyta ŁUKASIK, Emilia ŁABUĆ, ANALYSIS OF THE POSSIBILITY OF USING THE SINGULAR VALUE DECOMPOSITION IN IMAGE COMPRESSION , Applied Computer Science: Vol. 18 No. 4 (2022)
- Sri INDRA MAIYANTI, Anita DESIANI, Syafrina LAMIN, P PUSPITAHATI, Muhammad ARHAMI, Nuni GOFAR, Destika CAHYANA, ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION , Applied Computer Science: Vol. 19 No. 3 (2023)
- Zaid ALSAYGH, Zohair AL-AMEEN, CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM , Applied Computer Science: Vol. 18 No. 2 (2022)
- Sunil Kumar B L, Sharmila Kumari M, RGB-D FACE RECOGNITION USING LBP-DCT ALGORITHM , Applied Computer Science: Vol. 17 No. 3 (2021)
- Anusha NALLAPAREDDY, DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE , Applied Computer Science: Vol. 18 No. 1 (2022)
- Ziadeddine MAKHLOUF, Abdallah MERAOUMIA , Laimeche LAKHDAR, Mohamed Yassine HAOUAM , ENHANCING MEDICAL DATA SECURITY IN E-HEALTH SYSTEMS USING BIOMETRIC-BASED WATERMARKING , Applied Computer Science: Vol. 20 No. 1 (2024)
- Mohanad ABDULHAMID, Njagi KINYUA, SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE , Applied Computer Science: Vol. 16 No. 1 (2020)
- Nancy WOODS, Charles ROBERT, ENCAPSULATION OF IMAGE METADATA FOR EASE OF RETRIEVAL AND MOBILITY , Applied Computer Science: Vol. 15 No. 1 (2019)
You may also start an advanced similarity search for this article.
