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Prabusankarlal, K. M.
- Modified NLM Model for Despeckling Ultrasound Images Using FCM Clustering Based Pre Classification and RIBM
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Authors
Affiliations
1 Department of Electronics and Communication, K.S. Rangasamy College of Arts and Science, IN
2 Department of Electronics and Communication, Government Arts College, Dharmapuri, IN
3 Department of Information Technology, Arignar Anna Government Arts College, IN
4 Department of Mathematics, K.S.R. College of Arts and Science for Women, IN
1 Department of Electronics and Communication, K.S. Rangasamy College of Arts and Science, IN
2 Department of Electronics and Communication, Government Arts College, Dharmapuri, IN
3 Department of Information Technology, Arignar Anna Government Arts College, IN
4 Department of Mathematics, K.S.R. College of Arts and Science for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 3 (2018), Pagination: 1708-1715Abstract
Speckle noise is an inherent characteristic of ultrasound which reduces the classification accuracy of computer aided diagnosis (CAD) systems. A modified non local means (NLM) filter for despeckling ultrasound images is proposed in this article. The proposed NLM model utilizes a preclassification method in which the feature vectors of the input image are constructed using moment invariants and then they are clustered using fuzzy c means (FCM) algorithm. The rotationally invariant block matching (RIBM) algorithm is applied among the blocks within each cluster instead of the entire image. This intra cluster block matching reduces computational complexity of NLM process without the elimination of any pixel candidate. Further, the rotationally invariant moment distance measure improves the noise reduction performance of the algorithm by increasing the chance of getting more similar candidates for NLM process. Extensive experiments are conducted using synthetic images, phantom images and ultrasound images. The method is comparatively evaluated with other denoising methods using statistical parameters such as MSE, PSNR, SSIM, EPI and ENL. The quantitative results suggested that the proposed method outperforms other four state of the art methods in despeckling and preservation of image details.Keywords
Speckle Noise, Non Local Means, Fuzzy C Means, Ultrasound, CAD Systems.
References
- A. Achim, A. Bezerianos and, P. Tsakalides, “Novel Bayesian Multiscale method for Speckle Removal in Medical Ultrasound Images”, IEEE Transactions on Medical Imaging, Vol. 20, No. 8, pp. 772-783, 2001.
- K. Drukker, N.P. Gruszauskas, C.A. Sennett and M.L. Giger, “Breast US Computer-aided Diagnosis Workstation: Performance with a Large Clinical Diagnostic Population”, Radiology, Vol. 248, No. 2, pp. 392-397, 2008.
- K.M. Prabusankarlal, P. Thirumoorthy and R. Manavalan, “Segmentation of Breast Lesions in Ultrasound Images Through Multiresolution Analysis using Undecimated Discrete Wavelet Transform”, Ultrasonic Imaging, Vol. 38, No. 6, pp. 384-402, 2016.
- K.M. Prabusankarlal, P. Thirumoorthy and R. Manavalan, “Computer Aided Breast Cancer Diagnosis Techniques in Ultrasound: A Survey”, Journal of Medical Imaging and Health Informatics, Vol. 4, No 3, pp. 331-349, 2014.
- I. Njeh, O.B. Sassi, K. Chtourou and A.B. Hamida, “Speckle Noise Reduction in Breast ultrasound Images: SMU (SRAD Median Unsharp) Approach”, Proceedings of 8th IEEE International Conference on Systems, Signals and Devices, pp. 1-6, 2011.
- A.K. Jain, “Fundamental of Digital Image Processing”, Prentice-Hall, 1989. [7] O.V. Michailovich and A. Tannenbaum, “Despeckling of Medical Ultrasound Images”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency, Vol. 53, No. 1, pp. 64-78, 2006.
- Y. Guo, H.D. Cheng, J. Tian and Y. Zhang, “A Novel approach to Speckle Reduction in Ultrasound Imaging”, Ultrasound in Medicine and Biology, Vol. 35, No. 4, pp. 628-640, 2009.
- P. Perona and J. Malik, “Scale-Space and Edge Detection using Anisotropic Diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, pp. 629-639, 1990.
- G. Gerig, O. Kubler, R. Kikinis and F.A. Jolesz, “Nonlinear Anisotropic Filtering of MRI Data”, IEEE Transactions on Medical Imaging, Vol. 11, No 2, pp. 221-232, 1992.
- Y. Zhang, H.D. Cheng, J. Tian and J. Huang, “A Novel Speckle Reduction and Contrast Enhancement Method based on Fuzzy Anisotropic Diffusion”, Proceedings of IEEE 17th International Conference on Image Processing, pp. 4161-4164, 2011.
- Y. Yu and S.T. Acton, “Speckle Reducing Anisotropic Diffusion”, IEEE Transactions on Image Processing, Vol. 11, No. 11, pp. 1260-1270, 2002.
- A. Buades, B.Coll and J.M. Morel, “A Review of Image Denoising Algorithm, with a New One”, Available at: https://hal.archives-ouvertes.fr/hal-00271141/document.
- M. Mahmoudi and G. Sapiro, “Fast Image and Video Denoising via Nonlocal means of Similar Neighborhoods”, IEEE Signal Processing Letters, Vol. 12, pp. 839-842, 2005.
- J. Wang, Y. Guo, Y. Ying, Y. Liu and Q. Peng, “Fast Non-Local Algorithm for Image Denoising”, Proceedings of IEEE International Conference on Image Processing, pp. 1429-1432, 2006.
- P. Chao, O.C. Au, D. Jingjing, Y. Wen and Z. Feng, “A fast NL-means Method in Image Denoising based on the Similarity of Spatially Sampled Pixels”, Proceedings of IEEE International Workshop on Multimedia Signal Processing, pp. 1-4, 2009.
- Y.L. Liu, J. Wang, X. Chen and Y.W. Guo, “A Robust and Fast Non-Local Means Algorithm for Image Denoising”, Journal of Computer Science and Technology, Vol. 23, No. 2, pp. 270-279, 2008.
- S. Grewenig, S. Zimmer and J. Weickert, “Rotationally Invariant Similarity Measures for Nonlocal Image Denoising”, Journal of Visual Communication and Image Representation, Vol. 22, No. 2, pp. 117-130, 2011.
- Y. Gu, Z. Cui, C. Xiu and L. Wang, “Ultrasound Echocardiography Despeckling with Non-Local Means Time Series Filter”, Neurocomputing, Vol. 124, pp. 120-130, 2014.
- Y. Zhan, M. Ding, L. Wu and X. Zhang, “Nonlocal Means Method using Weight Refining for Despeckling of Ultrasound Images”, Signal Processing, Vol. 103, pp. 201-213, 2014..
- P. Coupe, P. Hellier, C. Kervrann and C. Barillot, “Nonlocal means-based Speckle Filtering for Ultrasound Images”, IEEE Transactions on Image Processing, Vol. 18, No. 10, pp. 2221-2229, 2009.
- R. Yan, L. Ling Shao, S.D. Cvetkovic and J. Klijn, “Improved Nonlocal means based on Pre-Classification and Invariant Block Matching”, IEEE Journal of Display Technology, Vol. 8, No. 4, pp. 212-218, 2012.
- M.K. Hu, “Visual Pattern by Moment Invariants”, IRE Transactions on Information Theory, Vol. 8, No. 1, pp. 179-187, 1962.
- K.S. Chuang, H.L. Tzeng, S. Chen, J. Wu and T.J. Chen, “Fuzzy C-Means Clustering with Spatial Information for Image Segmentation”, Computerized Medical Imaging and Graphics, Vol. 30, pp. 9-15, 2006.
- K.M. Prabusankarlal, P. Thirumoorthy and R. Manavalan, “Combining Clustering, Morphology and Metaheuristic Optimization Technique for Segmentation of Breast Ultrasound Images to Detect Tumors”, International Journal of Computer Applications, Vol. 86, pp. 28-34, 2014.
- Q. Li, H. Zhang and T. Wang, “Scale Invariant Feature Matching using Rotation-Invariant Distance for Remote Sensing Image Registration”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 27, No. 2, pp. 4-22, 2013.
- Ultrasound Simulation Program, Available at: http:// field-ii.dk/, Accessed on 2015.
- J.A. Jensen and N.B. Svendsen, “Calculation of Pressure Fields from Arbitrarily Shaped, Apodized, and Excited Ultrasound Transducers”, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 39, No. 2, pp. 262-267, 1992.
- Ultrasoundcases, Available at: http://ultrasoundcases.info/category.aspx?cat=67, Accessed on 2016
- K. Thangavel, R. Manavalan and I.L. Aroquiaraj, “Removal of Speckle Noise from Ultrasound Medical Image based on Special Filters: Comparative Study”, ICGST-GVIP Journal, Vol. 9, No. 3, pp. 25-32, 2009.
- K.M. Prabusankarlal, P. Thirumoorthy and R. Manavalan. “Classification of Breast Masses in Ultrasound Images using Self-Adaptive Differential Evolution Extreme Learning Machine and Rough Set Feature Selection”, Journal of Medical Imaging, Vol. 4, No. 2, pp. 245-249, 2017.