Custom Filter-based Enhancement of MRI Images for Impulse Noise
DOI:
https://doi.org/10.26713/cma.v15i3.2692Keywords:
Magnetic Resonance Imaging (MRI), Image enhancement, Impulse noise, Custom filter, Medical imaging, PSNR, SSIMAbstract
This paper introduces a novel approach for enhancing MRI images corrupted by impulse noise. Impulse noise often degrades image quality, especially in medical imaging applications where accurate representation is critical. The proposed method employs a custom filter to remove impulse noise while maintaining essential image features. The custom filter is designed by altering the average value of its surrounding pixels depending on whether the pixel is entirely black or completely white. The filter’s design is determined by the specifications of MRI images and noise patterns typically encountered in such data. The proposed custom filter exhibits the effective operation of the denoising technique and fully utilizes the benefits of the improved median filter in eliminating impulse noise. The results demonstrate an improvement in image quality based on various quality metrics. There is a 6% improvement in Peak Signal Noise Ratio, 0.5% in Mean Square Error, 1% in Structural Similarity Index Measure, 0.7% in Image Quality and 0.3% in Average Gradient compared to a conventional median filter. Experimental results demonstrate significant improvements in image quality and preservation of diagnostic information compared to existing methods. The proposed approach offers a promising solution for enhancing MRI images affected by impulse noise, thereby aiding in more accurate diagnosis and treatment planning in medical imaging.
Downloads
References
M. A. Aslam, M. A. Munir and D. Cui, Noise removal from medical images using hybrid filters of technique, Journal of Physics: Conference Series 1518 (2020), 012061 DOI: 10.1088/1742-6596/1518/1/012061.
P.-Y. Chen, C.-Y. Lien and H.-M. Chuang, A low-cost VLSI implementation for efficient removal of impulse noise, IEEE Transactions on Very Large Scale Integration (VLSI) Systems 18(3) (2010), 473 – 481, DOI: 10.1109/TVLSI.2008.2012263.
J. Das, B. Das, J. Saikia and S. R. Nirmala, Removal of salt and pepper noise using a selective adaptive median filter, in: Proceedings of the 2016 International Conference on Accessibility to Digital World (ICADW), Guwahati, India, 2016, pp. 203 – 206, IEEE (2016), DOI: 10.1109/ICADW.2016.7942542.
B. Deepa and M. G. Sumithra, Comparative analysis of noise removal techniques in MRI brain images, in: Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 2015, pp. 1 – 4, IEEE (2015), DOI: 10.1109/ICCIC.2015.7435737.
J. M. C. Geoffrine and N. Kumarasabapathy, Study and analysis of impulse noise reduction filters, Signal & Image Processing: An International Journal 2(1) (2011), 82 – 92, DOI: 10.5121/SIPIJ.2011.2107.
T. D. H. Hussein, K. H. Omar and H. K. Omar, A study on image noise and various image denoising techniques, ResearchJet Journal of Analysis and Inventions 2(11) (2020), 27 – 44.
P. Jain and V. Tyagi, A survey of edge-preserving image denoising methods, Information Systems Frontiers 18 (2016), 159 – 170, DOI: 10.1007/s10796-014-9527-0.
K. M. Jeevan and S. Krishnakumar, An algorithm for wavelet thresholding based image denoising by representing images in hexagonal lattice, Journal of Applied Research and Technology 16(2) (2018), 103 – 114, DOI: 10.22201/icat.16656423.2018.16.2.705.
C.-Y. Lien, C.-C. Huang, P.-Y. Chen and Y.-F. Lin, An efficient denoising architecture for removal of impulse noise in images, IEEE Transactions on Computers 62(4) (2013), 631 – 643, DOI: 10.1109/TC.2011.256.
M. Mehdi, H. Rajaei, M. Cabrerizo and M. Adjouadi, A robust edge detection approach in the presence of high impulse noise intensity through switching adaptive median and fixed weighted mean filtering, IEEE Transactions on Image Processing 27(11) (2018), 5475 – 5490, DOI: 10.1109/TIP.2018.2857448.
A. Pandey and K. K. Singh, Analysis of noise models in digital image processing, International Journal of Science, Technology & Management 4(1) (2015), 140 – 144.
K. Patil and V. Bendre, A comprehensive survey on image filtering & inpainting for improved image quality, in: Proceedings of the 2023 OITS International Conference on Information Technology (OCIT), Raipur, India, 2023, pp. 514-520, IEEE (2023), DOI: 10.1109/OCIT59427.2023.10430651.
K. Patil and V. Bendre, Image enhancement by cascading of filters for noise reduction, in: Proceedings of the International Conference on Computer Vision and Internet of Things 2023 (ICCVIoT’23), Coimbatore, India, 2023, pp. 97 – 101, IEEE (2023), DOI: 10.1049/icp.2023.2860.
Y. Qian, Image denoising algorithm based on improved wavelet threshold function and median filter, in: Proceedings of the 2018 IEEE 18th International Conference on Communication Technology (ICCT), Chongqing, China, 2018, pp. 1197 – 1202, (2018), DOI: 10.1109/ICCT.2018.8599921.
A. Roy, J. Singha, L. Manam and R. H. Laskar, Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images, IET Image Processing 11(6) (2017), 352 – 361, DOI: 10.1049/iet-ipr.2016.0320.
A. P. Sen, T. Pradhan, N. K. Rout and A. Kumar, Comparison of algorithms for the removal of impulsive noise from an image, e-Prime - Advances in Electrical Engineering, Electronics and Energy 3 (2023), 100110, DOI: 10.1016/j.prime.2023.100110.
A. Shah, J. I. Bangash, A. W. Khan, I. Ahmed, A. Khan, A. Khan and A. Khan, Comparative analysis of median filter and its variants for removal of impulse noise from gray scale images, Journal of King Saud University - Computer and Information Sciences 34(3) (2022), 505 – 519, DOI: 10.1016/j.jksuci.2020.03.007.
S. Shrestha, Image denoising using new adaptive-based median filters, Signal & Image Processing: An International Journal 5 (2014), 1 – 13, DOI: 10.5121/sipij.2014.5401.
X. Zhang, J. Li, J. Xing, P. Wang, Q. Yang and C. He, A particle swarm optimization technique based parametric wavelet thresholding function for signal denoising, Circuits, Systems, and Signal Processing 36 (2016), 247 – 269, DOI: 10.1007/s00034-016-0303-x.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a CCAL that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
