Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Denoising Medical Images Using Machine Learning, Deep Learning Approaches: A Survey

Author(s): Ali Arshaghi , Mohsen Ashourian* and Leila Ghabeli

Volume 17 , Issue 5 , 2021

Published on: 18 November, 2020

Page: [578 - 594] Pages: 17

DOI: 10.2174/1573405616666201118122908

Price: $65

Abstract

Objective: Several denoising methods for medical images have been applied, such as Wavelet Transform, CNN, linear and Non-linear methods.

Methods: In this paper, A median filter algorithm will be modified and the image denoising method to wavelet transform and Non-local means (NLM), deep convolutional neural network (Dn- CNN), Gaussian noise, and Salt and pepper noise used in the medical image is explained.

Results: PSNR values of the CNN method are higher and showed better results than different filters (Adaptive Wiener filter, Median filter, and Adaptive Median filter, Wiener filter).

Conclusion: Denoising methods performance with indices SSIM, PSNR, and MSE have been tested, and the results of simulation image denoising are also presented in this article.

Keywords: Medical denoising, NLM, PSNR, image processing, CNN, adaptive wiener filter.

Graphical Abstract
[1]
Rajinikanth V, Satapathy SC, Dey N, Fernandes SL, Manic KS. Skin melanoma assessment using kapur’s entropy and level set—A study with bat algorithm.Book Chapter: Smart Intelligent Computing and Applications. Springer. 193-202. 2019;pp.
[2]
Mondal T. Denoising and compression of medical image in wavelet 2D. Int J Recent Innov Trends Comput Commun 2015; 6: 173-8.
[3]
Mustafa N, Khan SA, Li JP, Khalil M, Kumar K, Mohaned G. Medical image de-noising schemes usingwavelet transform with fixed form thresholding. Int J Adv Comput Sci Appl 2015; 6: 173-8.
[4]
Bahendwar Y, Sinha GR. Efficient algorithm for denoising of medical images using discrete wavelet transforms. Math Methods Syst Sci Eng 2012; 142: 158-62.
[5]
Zhang X. Image denoising using shearlet transform and nonlinear diffusion. Proc Sci 2015; 20: 33-16.
[http://dx.doi.org/10.22323/1.259.0033]
[6]
Starck JL, Candès EJ, Donoho DL. The curvelet transform for image denoising. IEEE Trans Image Process 2002; 11(6): 670-84.
[http://dx.doi.org/10.1109/TIP.2002.1014998] [PMID: 18244665]
[7]
Hu J, Pu Y, Wu X, Zhang Y, Zhou J. Improved DCT-based nonlocal means filter for MR images denoising. Comput Math Methods Med 2012; 2012: 232685.
[http://dx.doi.org/10.1155/2012/232685] [PMID: 22545063]
[8]
Sameh Arif A, Mansor S, Logeswaran R. Combined bilateral and anisotropic-diffusion filters for medical image de-noising. IEEE Student Conf Res Dev SCOReD 2011; 2: 420-4.
[http://dx.doi.org/10.1109/SCOReD.2011.6148776]
[9]
Bhonsle D, Sinha GR, Chandra V. Medical image denoising using bilateral filter. Int J Image Gr Signal Process 2012; 4: 36-43.
[10]
Mousavi BS, Sargolzaei P, Razmjooy N, Hosseinabadi V, Soleymani F. Digital image segmentation using rule-base classifier. Am J Sci Res 2011; 35: 17-23.
[11]
Mousavi BS, Soleymani F, Razmjooy N. Color image segmentation using neuro-fuzzy system in a novel optimized color space. Neural Comput Appl 2013; 23(5): 1513-20.
[http://dx.doi.org/10.1007/s00521-012-1102-3]
[12]
Ali HM. MRI medical image denoising by fundamental filters. INTECH. 2018.
[http://dx.doi.org/10.5772/intechopen.72427]
[13]
Mohan J, Krishnaveni V, Guo Y. A new neutrosophic approach of Wiener filtering for MRI denoising. Meas Sci Rev 2013; 13(4)
[http://dx.doi.org/10.2478/msr-2013-0027]
[14]
Gonzalez RCWRH. Digital Image Processing. Upper Saddle River, NJ. 2. Prentice-Hall:2002;p.
[15]
Ertürk MA, Bottomley PA, El-Sharkawy AM. Denoising MRI using spectral subtraction. IEEE Trans Biomed Eng 2013; 60(6): 1556-62.
[http://dx.doi.org/10.1109/TBME.2013.2239293] [PMID: 23322757]
[16]
Filtering medical image using adaptive filter engineering in Medicine and Biology Society. Proceedings of the 23rd Annual International Conference of the IEEE 2015; 3: 2727-9.
[17]
Rashid Sheykhahmad F, Razmjooy N, Ramezani M. A novel method for skin lesion segmentation. Int J Inf Sec Sys Manage 2015; 4(2): 458-66.
[18]
Lin L, Meng X, Liang X. Reduction of impulse noise in MRI images using block-based adaptive median filter. IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE). 132-4.
[http://dx.doi.org/10.1109/ICMIPE.2013.6864519]
[19]
Moallem P, Razmjooy N. A multi layer perceptron neural network trained by invasive weed optimization for potato color image segmentation. Trends Appl Sci Res 2012; 7(6): 445.
[http://dx.doi.org/10.3923/tasr.2012.445.455]
[20]
Moallem P, Razmjooy N, Ashourian M. Computer vision-based potato defect detection using neural networks and support vector machine. Int J Robot Autom 2013; 28(2): 137-45.
[http://dx.doi.org/10.2316/Journal.206.2013.2.206-3746]
[21]
Moallem P, Razmjooy N, Mousavi B. Robust potato color image segmentation using adaptive fuzzy inference system. Iranian J Fuzzy Syst 2014; 11(6): 47-65.
[22]
Arshaghi A, Nooshyar M, Ashourian M. Image transmission in MIMO-UWB systems using Multiple Description Coding (MDC) over AWGN and fading channels with DS-PAM modulation. World Essays J 2017; 5: 12-24.
[23]
Arshaghi A. Data and image transmission on DS-PAM UWB system in parallel links AWGN channel using Multiple Description Coding (MDC). Int Res J Appl Basic Sci 2014; 8(6): 717-26.
[24]
Bhatnagar S, Jain RC. Different denoising techniques for medical images in wavelet domain. International Conference on Signal Processing And Communication (ICSC). 325-9.
[http://dx.doi.org/10.1109/ICSPCom.2013.6719806]
[25]
Buades A. A non-local algorithm for image denoising. Comput Vision Pattern Recogn 2005; 2: 60-5.
[26]
Buades A, Coll B, Morel JM. A non-local algorithm for image denoising. IEEE Comput Soc Confer Comput Vision Pattern Recognit 2005; 2(7): 60-5.
[27]
Buades A, Coll B, Morel JM. A review of image denoising algorithms, with a new one. Multiscale Model Simul 2005; 4: 490-530.
[http://dx.doi.org/10.1137/040616024]
[28]
Mahmoudi M, Sapiro G. Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Process Lett 2005; 12: 839-42.
[http://dx.doi.org/10.1109/LSP.2005.859509]
[29]
Buades A, Coll B, Morel JM. On image denoising methods. 1-40.
[30]
Bengio Y, Lamblin P, Popovici D, Larochelle H. Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 2007; 61: 85-117.
[31]
Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw 2015; 61: 85-117.
[http://dx.doi.org/10.1016/j.neunet.2014.09.003] [PMID: 25462637]
[32]
Ye S, Wei H, Chen Y. Design for medical imaging services platform based on cloud computing. Int J Big Data Intell 2016; 3: 270-8.
[http://dx.doi.org/10.1504/IJBDI.2016.10000790]
[33]
Vincent P. Extracting and composing robust featureswith denoising autoencoders. IEEE Student Conference on Research and Development. 1096-3.
[34]
Gondara L. Medical image denoising using convolutional denoising autoencoders. In: IEEE Conference on Computer Vision and Pattern Recognition 17.
[http://dx.doi.org/10.1109/ICDMW.2016.0041]
[35]
Zhang K. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. Tech report Computer Vision and Pattern Recognition 2016; 1-13.
[36]
Girshick NR. Fast R-CNN. Int Conf Comput Vis Pattern Recogn 2015;pp; 1440-8.
[37]
Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision Pattern Recognition. 580-7.
[http://dx.doi.org/10.1109/CVPR.2014.81]
[38]
Oliveira TP, Barbar JS, Soares AS. Computer network traffic prediction: a comparison between traditional and deep learning neural networks. Int J Big Data Intell 2016; 3: 28-37.
[http://dx.doi.org/10.1504/IJBDI.2016.073903]
[39]
Ghoneim A, Muhammad G, Amin SU, Gupta B. Medical image forgery detection for smart healthcare. IEEE Commun Mag 2018; 56(4): 33-7.
[http://dx.doi.org/10.1109/MCOM.2018.1700817]
[40]
Golea NH, Melkemi KE. ROI-based fragile watermarking for medical image tamper detection. Int J High Perform Comput Network 2019; 13(2): 199.
[http://dx.doi.org/10.1504/IJHPCN.2019.097508]
[41]
Dorgham OM, Al-Rahamneh B, Almomani A, Khalaf K. Enhancing the security of exchanging and storing DICOM medical images on the cloud. Int J Cloud Appl Comput 2018; 8(1): 154-72.
[http://dx.doi.org/10.4018/IJCAC.2018010108]
[42]
Guo P, Bhattacharya P, Evans A. Nuclei segmentation for quantification of brain tumors in digital pathology images. Int J Softw Sci Comput Intell 2018; 10(2): 36-49.
[http://dx.doi.org/10.4018/IJSSCI.2018040103]
[43]
Liu H, Guo Q, Wang G, Gupta BB, Zhang C. Medical image resolution enhancement for healthcare using nonlocal self-similarity and low-rank prior. Multimedia Tools Appl 2019; 78: 9033-50.
[http://dx.doi.org/10.1007/s11042-017-5277-6]
[44]
Kaur P, Singh G, Kaur P. A review of denoising medical images using machine learning approaches. Curr Med Imaging Rev 2018; 14(5): 675-85.
[http://dx.doi.org/10.2174/1573405613666170428154156] [PMID: 30532667]
[45]
Gagandeep K. A wavelet approach for medical image denoising. IJARCS 2017; 8(8): 46-50.
[http://dx.doi.org/10.26483/ijarcs.v8i8.4621]
[46]
Kamble VM. Performance evaluation of wavelet, ridgelet, curvelet and contourlet transforms based techniques for digital image denoising. Artif Intell Rev 2016; 45: 509-33.
[http://dx.doi.org/10.1007/s10462-015-9453-7]
[47]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88.
[http://dx.doi.org/10.1016/j.media.2017.07.005] [PMID: 28778026]
[48]
Fabrizio R. On the accuracy of denoising algorithms in medical imaging: A case study. International Symposium on Medical Measurements and Applications (MeMeA).
[49]
Oludayo OO. Segmentation of melanoma skin lesion using perceptual color difference saliency with morphological analysis. Hindawi Mathematical Problems in Engineering 2018; 2018: Article ID 1524286.
[50]
Worku J, Feng J, Seungmin R, Maowei C, Shaohui L. Medical image denoising using convolutional neural network: a residual learning approach. J Supercomput 2019; 75: 704-18.
[http://dx.doi.org/10.1007/s11227-017-2080-0]
[51]
Romdhane F, Faouzi B, Amiri H. 3D medical images denoising. IEEE IPAS’14: International Image Processing Applications and Systems Conference.
[http://dx.doi.org/10.1109/IPAS.2014.7043298]
[52]
Mangai JA, Navak J, Kumar VS. A novel approach for classifying medical images using data mining techniques. Int J Comp Sci Elec Engineer 2013; 1(2): 188-92.
[53]
Antonini M, Barlaud M, Mathieu P, Daubechies I. Image coding using wavelet transform. IEEE Trans Image Process 1992; 1(2): 205-20.
[http://dx.doi.org/10.1109/83.136597] [PMID: 18296155]
[54]
Ali SA, Vathsal S, Kishore KL. A GA-based window selection methodology to enhance window-based multi-wavelet transformation and thresholding aided CT image denoising technique. Int J Comput Sci Inf Secur 2010; 7(2): 280-8.
[55]
Malik M, Ahsan F, Mohsin S. Adaptive image denoising using cuckoo algorithm. Soft Comput 2014; 20(3): 925-38.
[http://dx.doi.org/10.1007/s00500-014-1552-x]
[56]
Pereira DC, Ramos RP, do Nascimento MZ. Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Programs Biomed 2014; 114(1): 88-101.
[http://dx.doi.org/10.1016/j.cmpb.2014.01.014] [PMID: 24513228]
[57]
Mahmoud AA. Mixed curvelet andwavelet transforms for speckle noise reduction in ultrasonic B-mode images. Inform Sci Comp 2015; 1-21.
[58]
Liu Y. Image denoising method based on threshold, wavelet transform and genetic algorithm. Int J Sig Process Image Process Patt Recog 2015; 8(2): 29-40.
[http://dx.doi.org/10.14257/ijsip.2015.8.2.04]
[59]
MA. Comparative study of classification algorithms in ehealth environment. Sixth International Conference on Digital Information Processing and Communications (ICDIPC). 42-7.
[60]
Shan J, Alam SK, Garra B, Zhang Y, Ahmed T. Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods. Ultrasound Med Biol 2016; 42(4): 980-8.
[http://dx.doi.org/10.1016/j.ultrasmedbio.2015.11.016] [PMID: 26806441]
[61]
de Bruijne M. Machine learning approaches in medical image analysis: From detection to diagnosis. Med Image Anal 2016; 33: 94-7.
[http://dx.doi.org/10.1016/j.media.2016.06.032] [PMID: 27481324]
[62]
Ravishankar H, Prabhu SM, Vaidya V, Singhal N. Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning. IEEE Conference. Prague, Czech Republic. 779-82.
[http://dx.doi.org/10.1109/ISBI.2016.7493382]

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy