IMAGE ENHANCEMENT AND DE-NOISING TECHNIQUES OF MAGNETIC RESONANCE IMAGES
DOI:
https://doi.org/10.11113/aej.v12.18145Abstract
Medical images have noise, such as salt and pepper, speckle, and gaussian noise. So, getting the accuracy of magnetic resonance images is challenging always. The accuracy of brain images is essential for the clinical diagnosis process. The nonlinear method of median and morphological filters is used for enhancement, contrast adjustment, and histogram equalization for medical images and speech processing, to preserve them from noise and edge features. Because of its edge-preserving properties, this median spatial filter window is critically important and better for removing impulsive noise and speckle noise. Nine different shapes of windows and five different sizes of windows and five noise density injections are used for evaluating the performance of the median filter. The first person J.W.Tukey, did the smoothing process by median rank selection filter. The denoising using morphological process is also considered for analysis. These two nonlinear methods like median filtering and morphological methods are used for the restoration of noise from the original image. These two methods are applied in normal and seizure-affected images to calculate quality assessment metrics and also the performance evaluation metrics from magnetic resonance images. Any image needs to be denoised to get the accuracy, precision, and F-measure kind of common feature evaluation metrics. This paper evaluates two nonlinear filtering methods and their performance evaluated based on shapes and size of windows and for suppressing or de-noising to produce original noise-free images to assist clinically.
References
R. C. Gonzalez and R. E. Woods.2018. Digital Image processing, 4th Edition (Prentice-Hall) ISBN.
Bhausaheb Shinde., Dnyandeo Mhaske., Mahindra Patare., A.R. Dani., A.R. Dani.2012. Apply Different Filtering Techniques to Remove the Speckle Noise Using Medical Images, International Journal of Engineering Research and Applications, 2(1): 1071-1079.
Mahmoud-Ghoneim D, Toussaint G, Constans JM, and De Certaines JD.2008 “Three-dimensional texture analysis in MRI: a preliminary evaluation in gliomas, Magn Reson Imaging, 21: 983-7.
M. Lysaker., A. Lundervold., and X. Tai. 2003.Noise removal using fourth- order partial differential equation with applications to medical magnetic resonance images in space and time, IEEE Transactions on Image Processing.
Kaur., Heminder. 2019.U-Healthcare Monitoring Systems, A genetic algorithm-based metaheuristic approach to customize a computer-aided classification system for enhanced screen film mammograms,” 217–259 .
Motwani M. C., Gadiya., R. C. Harri.S., Survey of Image Denoising Techniques, University of Nevada, Reno Dept of Comp. Sci. & Engr., Reno, NV 89557 USA (775): 784-6571.
Suhas S. and C R Venugopal.2018. An Efficient MRI Noise Removal Technique using Linear and Nonlinear Filters, International Journal of Computer Applications, 179(15): 17-20.
Nguyen Thanh Binh and Ashish Khare.2015. Adaptive complex wavelet technique for medical image denoising, in proceedings of the third International Conference on the development of Biomedical Engineering, Vietnam, 11(14): 195-198.
Manjon JV, Coupe P, Buades A.2015. MRI noise estimation and de-noising using non-local PCA. Medical Image Analysis. 22(1): 35-47.
Ai D, Yang J, Fan J, Cong W, Wang X.2015. Denoising filters evaluation for magnetic resonance images, Optik, 126(23): 3844-50.
Liu Y, Ma C, Clifford BA, Lam F, Johnson CL, Liang ZP. 2016.Im-proved low-rank filtering of magnetic resonance spectroscopic im-aging data corrupted by noise and field inhomogeneity,” IEEE Transactions on Bio-Medical Engineering 63(4): 841-9.
Hong Y, Ren G, Liu E.2016.A no-reference image blurriness metric in the spatial domain, ”Optik. 127: 5568-5575.
Hancer,E., Ozturk,C., and Karaboga,D.2013. Extraction of brain tumors from MRI images with artificial bee colony-based segmentation methodology, 8th International Conference on Electrical Engineering and Electronics, 516–520.
Gudbjartsson,H., and Patz.S.1995. The Rician distribution of noisy MRI data.,” Magnetic Resonance in Medicine, 34(6): 910–4.
Yousuf.M.a., and Nobi,M.M. 2010. A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images, Journal of Scientific Research, 3(1): 81–88.
Sivasundari, M. K. S., Siva Kumar, R.2014. Performance Analysis of Image Filtering Algorithms for MRI Images, International Journal of Research in Engineering and Technology, 3(5): 438–440.
Luo, S.2001.Filtering medical image using adaptive filter, Engineering in Medicine and Biology Society, Proceedings of the 23rd Annual International Conference of the IEEE,2001, 3: 2727-2729.
Lin,L., Meng, X., Liang,X.2013. Reduction of impulse noise in MRI images using block-based adaptive median filter,Medical Imaging Physics and Engineering (ICMIPE), IEEE International Conference , 132-134.
Priyadharsini, B.2014. A Novel Noise Filtering Technique for De-noising MRI Images, Proceedings of International Conference on Global Innovations in Computing Technology (ICGICT’14), 2, Special Issue 1.
Islam, M. R., Imteaz, and Marium-E-Jannat. 2018.Detection and analysis of brain tumor from MRI by Integrated Thresholding and Morphological Process with Histogram based method, International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), 1-5.
P. A. Lyakhov, A. R. Orazaev, N. I. Chervyakov and D. I. Kaplun, 2019, "A New Method for Adaptive Median Filtering of Images," 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Saint Petersburg and Moscow, Russia, 1197-1201, doi: 10.1109/EIConRus.2019.8657050.
Virmani, J., Kumar, V., Kalra,N., Khandelwal,N.2013. Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound, International Journal of Convergence Computing.1(1): 19–37.
Sharma,K., Virmani,J.2017,A decision support system for classification of normal and medical renal disease using ultrasound images: a decision support system for medical renal diseases, International Journal of Ambient Computing and Intelligence, 8(2): 52–69.
Chen, YY., Pock, T.2017.Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration, IEEE Transactions on Pattern Analysis and Machine Intelligence 39(6): 1256–1272.
Soong-Der Chen., Ramli, A.R.2003. Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation,” 49(4): 0–1309.
Satpathy, S.K,. Panda, K., Nagwanshi,K., and ArdilC.,, 2010. “Image Restoration in Non-Linear Filtering Domain using MDB Approach, International Journal of Signal Processing 6:1
Bouaynaya,N., and Schonfeld,D.2008.Theoretical foundations of spatially-variant mathematical morphology – Part II: Gray level images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 30: 837-850.
Soille, P., and Talbot, H.2001.Directional morphological filtering, IEEE Transactions on Pattern. Analysis Machine Intelligence,” 23(11): 1313–1329.
Hamid, M.S., Harvey, N.R., and Marshall, S.2003.Genetic algorithm optimization of multidimensional grayscale soft morphological filters with applications in film archive restoration, IEEE Transactions on Circuits System Video Technology, 13(5): 406–416.
Abdelhamid El hassani, Aicha Majda .2016, Efficient image denoising method based on mathematical morphology reconstruction and the Non-Local Means filter for the MRI of the head, Conference: 4th International Colloquium on Information Science and Technology.
Akar SA.2016, Determination of optimal parameters for bilateral filter in brain MR image denoising, Applied Soft Computing, 43: 87-96.
Petrou,M. C.2010. Image Processing the Fundamentals, John Wiley & Sons, Singapore.
Esakkirajan,S..2011. Removal of high-density salt and pepper noise through modified decision based unsymmetric trimmed median filter IEEE Signal Process, 8(5): 287-290.
Novoselac, V., Pavic, Z.2015 Adaptive center weighted median filter,” In: 7th International Scientific and Expert Conference TEAM, CT.
Das, J., Das, B., Saikia, J., Nirmala, S.R.2016.Removal of salt and pepper noise using selective adaptive median filter,International Conference on Accessibility to Digital World (ICADW), 16–18.
Sathua,S.K., Dash, A.2017.Behera Removal of salt and pepper noise from gray-scale and color images: an adaptive approach, Jan-Feb International Journal of Computer Science and Information Technology (IJCST) 5(1): 117-126.
Selvi, A.S., Sukumar, R.2018.Removal of salt and pepper noise from images using hybrid filter (HF) and fuzzy logic noise detector (FLND), Special. Issue: Advanced Algorithms IoT Cloud Computing Cyber Enabled Application (ICAMMAET-ICTPACT), 31(12).
Ramachandran, V., Kishorebabu,V.2019. A tri-state filter for the removal of salt and pepper noise in mammogram images, Journal of Medical System. 43(2): 1-10.
Sriparna Banerjee, and Sirrundhati Misra.2020.Advances in Smart Communication Technology and Information Processing,” Book, OPTRONIX, 393-406.
Ashpreet, and Mantosh Biswas.2021. A comparative study of median based impulse noise reduction methods for color images,” ICTACT Journal on Image and video processin. 12, issue 1, pp.2541-2554.
[41] Nidhal Bouaynaya, Mohammed Charif-Chefchaouni, and Dan Schonfeld, 2006.Spatially Variant Morphological Restoration and Skeleton Representation, Ieee Transactions On Image Processing, 15(11).
Lin, Po-Hsiung, Chen, Bo-Hao; Cheng, Fan-Chieh; Huang, Shih-Chia,2015. A Morphological Mean Filter for Impulse Noise Removal. Journal of Display Technology, 1–1.
Rajeesh J, Moni RS, Palanikumar S, Gopalakrishnan T, 2010.Noise reduction in magnetic resonance images using wave atom shrinkage, International Journal of Image Processing (IJIP), 4(2): 131-141.
Zhang M, Gunturk BK, 2008.Multi resolution bilateral filtering for image de-noising,” IEEE Transactions on Image Processing, 17(12): 2324-2333.
Phophalia, A., Rajwade, A. 2014.Mitra SK. Rough set-based image de-noising for brain MR images, Signal Processing, 103: 24-35.
Isa. IS., Sulaiman, SN., Mustapha, M., Darus, S. 2015.Evaluating de-noising performances of fundamental filters for T2-weighted MRI images,19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems. Procedia Computer Science, 60: 760-768.
Rahmat, R., Mali,k AS., Kamel, N, 2013.Comparison of LULU and median filter for image de-noising, International Journal of Computer and Electrical Engineering, 5(6): 568-571.
Hanafy M. Ali.2017.MRI Medical Image Denoising by Fundamental Filters, SCIREA Journal of Computer, 2(1): 12-26.
Appiah,O., Martey.E.M., and Quayson,E.2019.Effect of Window's Shape on Median Filtering, Ieee Africon, 1-8.
Mahmoud,T.A., Marshall.S, 2008.Medical image enhancement using threshold decomposition driven adaptive morphological filter, Proceedings of the 16th European Signal Processing Conference, Lausanne, Switzerland, 1–5.
Jagannath,H.S., Virmani,J., Kumar,V.2012. Morphological enhancement of microcalcifications in digital mammograms,” J. Inst. Eng. B (India) A genetic algorithm-based metaheuristic approach, chapter 10, 93(3): 163–172.
Sridhar, K.V.,Reddy, Prasad,A.M.,2015. Automatic detection of micro calcifications in a small field digital mammography using morphological adaptive bilateral filter and radon transform based methods,” Advanced Science Engineering and Medicine, 6(12): 1290–129
