Image Denoising using Hybrid Filter in Presence of Multiple Noises and Graphical User Interface for Medical Image Enhancement

Gopi Karnam*, T. Ramashri**
* Assistant Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India.
** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India.
Periodicity:October - December'2015
DOI : https://doi.org/10.26634/jip.2.4.3686

Abstract

In the field of image processing, the filtering algorithms are functional over the noisy images to eliminate the noise and protect the image details. In medical diagnosis, removing noise is a very challenging issue, as images are corrupted by multiple noises. Medical images like CT, MRI, and PET have information about the heart, nerves and brain. These images are to be precise and free from noise. This paper presents an efficient method for noise reduction, contrast enhancement for medical images. The projected method uses Hybridization of adaptive median filter with the wiener filter for denoising multiple noises. Wiener filter have enhanced stability between smoothness and precision. It also shows the GUI representation of Image smoothing, Histogram Equalization. The method is experimented on the MRI (Magnetic Resonance Image) and performance is evaluated in terms of the Peak Signal to Noise Ratio (PSNR), Correlation coefficient, Mean Absolute Error (MAE) and Mean Square Error (MSE). The proposed technique removes the Gaussian noise, Impulse noise and Blurredness in the images and improve the quality of images. The result shows that, the hybrid filter outperforms most of the basic algorithms for reduction of multiple noises in medical images. Finally, the results proved that the exploitation of hybrid filter gives the appropriate and consistent results on the test images and provide precision to picture while preserving its information.

Keywords

Adaptive Median Filter, Gaussian Noise, GUI, Hybrid Filter, Image Denoising, Mean Absolute Error, Mean Square Error, PSNR, Salt and Pepper Noise.

How to Cite this Article?

Karnam, G. and Ramashri, T. (2015). Image Denoising using Hybrid Filter in Presence of Multiple Noises and Graphical User Interface for Medical Image Enhancementt. i-manager’s Journal on Image Processing, 2(4), 10-18. https://doi.org/10.26634/jip.2.4.3686

References

[1]. R. Riji, Jeny Rajan, Jan Sijbers, and Madhu S. Nair (1892). Iterative bilateralfilter for Rician noise reduction in MR images, Springer 2014.J.
[2]. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., Vol. 2. Oxford: Clarendon, pp.68-73.
[3]. Susmitha Vekkot, and Pancham Shukla, (2009). “A Novel Architecture for Wavelet based Image Fusion”, World Academy of Science Engineering and Technology, No. 57, pp. 372-377.
[4]. Jenny Rajan, and M.R Kaimal (2006). “Image Denoising Using Wavelet Embedded anisotropic Diffusion”, Proceedings of IEE International Conference on Visual Information Engineering, pp. 589 – 593.
[5]. Abha Choubey, and G.R. Sinha, (2011). “Hybrid Filtering Technique in Medical Image Denoising: Blending of Neural Network and Fuzzy Inference”. IEEE, Vol. 1, pp. 170-177.
[6]. Sandeep dubey, Fehreen hasan, and Gaurav shrivasrava (2012). “A hybrid method for image denoising based on wavelet thresholding and RBF network”, International Journal of Advanced Computer Research , Vol. 2, No. 2, pp. 167-172.
[7]. Vivek Singh Bhadouria, Dibyendu Ghosal, and Abul Hasan Siddiqi (2013). “A new approach for high density saturated impulse noise removal usingdecision-based coupled window median filter”, Springer, Vol. 8, No. 1, pp. 71-84.
[8]. R. Vanithamani, and G. Umamaheswari (2014). “Speckle reduction in ultrasound images using neighshrink and bilateral filtering”. Journal of Computer Science, Vol. 10, No. 4, pp. 623-631.
[9]. Pierrick Coupé, Pierre Hellier, Charles Kervrann and Christian Barillot, (2009). “Nonlocal Means-Based Speckle Filtering for Ultrasound Images”, IEEE transactions on Image Processing, Vol. 18, No. 10.
[10]. Guodong Wang, Zhenkuan Pan, Zengfang Zhao, and Xiaotong Sun, (2010). “The Split Bregman Method of Image Decomposition Model for Ultrasound Image Denoising”, IEEE International Conference on Biomedical Engineering and Informatics, pp. 644-647.
[11]. S. Setzer, (2011). “Operator Splittings, Bregman Methods and Frame Shrinkage in Image Processing”, International Journal of Computer Vision, Vol. 92, No 3, pp. 265–280,
[12]. Thangavel, Manavalan and Laurence Aroquiaraj, (2009). "Removal of Speckle Noise from Ultrasound Medical Image based on Special Filters: Comparative Study", International Journal on Graphics, Vision and Image Processing, Vol. 9, No. 3, pp. 25-32.
[13]. J S Bhat, B N Jagadale, and Lakshminarayan H K, (2010). “Image De-noising with an Optimal Threshold using Wavelets”, International Conference on Image and Signal Processing (ICSIP).
[14]. LIU Wei, (2009). “New Method for Image Denoising while Keeping Edge Information”, 2nd International Conference on Image Processing CISP 2009.
[15]. S. Sudha , G. R. Suresh and R. Sukanesh, (2009). “Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance”. International Journal of Computer Theory and Engineering, Vol. 1, No. 1, pp.1793-8201.
[16]. Ioana Firoiu, Corina Nafornita, Jean-Marc Boucher, and Alexandru Isar, (2009). “Image Denoising Using a New Implementation of the Hyperanalytic Wavelet Transform”, IEEE Transaction on Instrument & Measurement, Vol. 58, No. 8.
[17]. Li Hongqiao, and Wang Shengqian, (2009). “A New Image Denoising Method Using Wavelet Transform”, International Forum on Information Technology and Application 2009.
[18]. S. Kother Mohideen, S. Arumuga Perumal, and M. Mohamed Sathik, (2008). “Image De-noising using Discrete Wavelet transform”. IJCSNS International Journal of Computer Science and Network Security, Vol. 8, No.1,
[19]. W. Zuo, L. Zhang, C. Song, and D. Zhang, (2013). “Texture enhanced image denoising via gradient histogram preservation”. in Proc. Int. Conf CVPR, pp. 1203–1210.
[20]. H. C. Burger, C. J. Schuler, and S. Harmeling, (2012). “Image denoising: Canplain neural networks compete with BM3D?”, in Proc. Int. Conf. CVPR, pp. 2392–2399.
[21]. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, (2009). “Non-local sparse models for image restoration”, in Proc. Int. Conf. Comput. Vis., pp. 2272–2279.
[22]. W. Dong, L. Zhang, G. Shi, and X. Li, (2013). “Nonlocally centralized sparse representation for image restoration”, IEEE Trans. Image Process., Vol. 22, No. 4, pp. 1620–1630.
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