The week's pick
Random Articles
Reseach Article
Speckle Reduction in Carotid Artery Images using Dual Tree Complex Wavelet Transform
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 52 |
| Year of Publication: 2018 |
| Authors: V. N. Prudhvi Raj, G. Geetha |
10.5120/ijca2018917366
|
V. N. Prudhvi Raj, G. Geetha . Speckle Reduction in Carotid Artery Images using Dual Tree Complex Wavelet Transform. International Journal of Computer Applications. 180, 52 ( Jun 2018), 1-6. DOI=10.5120/ijca2018917366
Abstract
Carotid artery disease is a condition in which the carotid arteries become narrowed or blocked. When the arteries become narrowed, the condition is called carotid stenosis. The carotid arteries provide the main blood supply to the brain. Carotid artery disease occurs when sticky, fatty substances called plaque build-up in the inner lining of the arteries. The plaque may slowly block or narrow the carotid artery or cause a clot (thrombus) to form more suddenly. Clots can lead to stroke. Imaging techniques have long been used for assessing and treating cardiac and carotid disease. B-mode ultrasound imaging or intravascular ultrasound (IVUS) has emerged, and it is widely used for visualizing carotid plaques and assessing plaque characteristics that are related to the onset of neurological symptoms. In Medical diagnosis operations such as feature extraction and object recognition will play the key role. These tasks will become difficult if the images are corrupted with noises. So the development of effective algorithms for noise removal became an important research area in present days. Developing Image denoising algorithms is a difficult task since fine details in a medical image embedding diagnostic information should not be destroyed during noise removal. The ultrasound imaging suffers from speckle noise. Many of the wavelet based denoising algorithms use DWT (Discrete Wavelet Transform) in the decomposition stage which is suffering from shift variance and lack of directionality. To overcome this in this paper we are using the denoising method which uses dual tree complex wavelet transform to decompose the image and we performed the shrinkage operation to eliminate the noise from the noisy image. In the shrinkage step we used semi-soft thresholding operator along with traditional hard and soft thresholding operators and verified the suitability of dual tree complex wavelet transform for the denoising of ultrasound images. The results proved that the denoised image using DTCWT (Dual Tree Complex Wavelet Transform) have a better balance between smoothness and accuracy than the DWT and less redundant than UDWT (UndecimatedWavelet Transform).We used the SSIM (Structural similarity index measure) along with PSNR to assess the quality of denoised images . [1],[2],[3]
References
- A. Bovik, Handbook of Image and Video Processing. Communications, Networking and Multimedia, Elsevier Science, 2010.
- R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2006.
- R. Rangayyan, Biomedical Image Analysis. Biomedical Engineering, CRC Press, 2004.
- V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PAMI- 4, pp. 157–166, march 1982.
- R. M. Rangayyan, M. Ciuc, and F. Faghih, “Adaptiveneighborhood filtering of images corrupted by signaldependent noise,” Appl. Opt., vol. 37, pp. 4477–4487, Jul 1998.
- R. B. Paranjape, R. M. Rangayyan, and W. M. Morrow, “Adaptive neighborhood mean and median image filtering,” Journal of Electronic Imaging, vol. 3, no. 4, pp. 360–367, 1994.
- M. I. Sezan and A. M. Tekalp, “Survey of recent developments in digital image restoration,” Optical Engineering, vol. 29, no. 5, pp. 393–404, 1990.
- R. M. Rangayyan and A. Das, “Filtering multiplicative noise in images using adaptive region-based statistics,” Journal of Electronic Imaging, vol. 7, no. 1, pp. 222–230, 1998.
- M. A. Schulze, “Edge-enhancing nonlinear filter for reducing multiplicative noise,” pp. 46–56, 1997.
- A. I. Qureshi, O. Saeed, and A. A. Malik, “Abstract 85: Is progressively worsening severity of internal carotid artery stenosis by carotid doppler ultrasound in asymptomatic patients a risk factor for ischemic stroke?,” vol. 49, no. Suppl 1, pp. A85–A85, 2018.
- G. Strang and T. Nguyen, Wavelets and Filter Banks. Wellesley-Cambridge Press, 1996.
- J. L. Starck, J. Fadili, and F. Murtagh, “The undecimated wavelet decomposition and its reconstruction,” IEEE Transactions on Image Processing, vol. 16, pp. 297–309, Feb 2007. Fig. 7: Denoised Image with Complex Discrete Wavelet Transforms with semisoft thresholding
- I. W. Selesnick, R. G. Baraniuk, and N. C. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Processing Magazine, vol. 22, pp. 123–151, Nov 2005.
- V. N. P. Raj and T. Venkateswarlu, “Denoising of medical images using dual tree complex wavelet transform,” Procedia Technology, vol. 4, pp. 238 – 244, 2012. 2nd International Conference on Computer, Communication, Control and Information Technology( C3IT-2012) on February 25 - 26, 2012.
- F. Russo, “An image-enhancement system based on noise estimation,” Instrumentation and Measurement, IEEE Transactions on, vol. 56, pp. 1435 –1442, aug. 2007.
- S. Aja-Fernndez, G. Vegas-Snchez-Ferrero, M. Martn- Fernndez, and C. Alberola-Lpez, “Automatic noise estimation in images using local statistics. additive and multiplicative cases,” Image and Vision Computing, vol. 27, no. 6, pp. 756 – 770, 2009.
- D. L. Donoho, I. M. Johnstone, G. Kerkyacharian, and D. Picard, “Wavelet shrinkage: Asymptopia?,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 57, no. 2, pp. 301–369, 1995.
- S.-M. Yang and S.-C. Tai, “A design framework for hybrid approaches of image noise estimation and its application to noise reduction,” Journal of Visual Communication and Image Representation, vol. 23, no. 5, pp. 812 – 826, 2012.
- G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, and A. Lanzolla, “Linear filtering of 2-d wavelet coefficients for denoising ultrasound medical images,” Measurement, vol. 45, no. 7, pp. 1792 – 1800, 2012.
- S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, no. 9, pp. 1532– 1546, 2000.
- C. P. Loizou, C. S. Pattichis, C. I. Christodoulou, R. S. H. Istepanian, M. Pantziaris, and A. Nicolaides, “Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 52, pp. 1653–1669, Oct 2005.
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, pp. 600–612, April 2004.
- C. Loizou, C. Pattichis, R. Istepanian, M. Pantziaris, E. Kyriakou, T. Tyllis, and A. Nicolaides, “Ultrasound image quality evaluation,” in Information Technology Applications in Biomedicine, 2003. 4th International IEEE EMBS Special Topic Conference on, pp. 138 – 141, april 2003.
Index Terms
Keywords