JSIP  Vol.3 No.1 , February 2012
A Hybrid De-Noising Method on LASCA Images of Blood Vessels
Abstract: A de-noising approach is proposed that based on the combination of wiener filtering, nonlinear filtering and wavelet fusion, which de-noise the LASCA (LAser Speckle Contrast Analysis) image of blood vessels in Small Animals. The approach first performs laser spectral contrast analysis on brain blood flow in rats, get their spatial and temporal contrast images. Then, a de-noising filtering method is proposed to deal with noise in LASCA. The image restoration is achieved by applying the proposed admixture filtering, and the subjective estimation and objective estimation are given to the de-noising images. As our experimental results shown, the proposed method provides clearer subjective sense and improved to over 25 db for PSNR.
Cite this paper: C. Wu, N. Feng, K. Harada and P. Li, "A Hybrid De-Noising Method on LASCA Images of Blood Vessels," Journal of Signal and Information Processing, Vol. 3 No. 1, 2012, pp. 92-97. doi: 10.4236/jsip.2012.31012.

[1]   Q. Liu, S.-B. Zhou, Z.-H. Zhang and Q.-M. Luo, “Application of Laser Speckle Imaging: Monitoring Changes of Vessels in Photodynamic Therapy,” Chinese Journal of Lasers, Vol. 32, No. 6, 2005, pp. 869-872.

[2]   S. S. Liu, P. C. Li and Q. M. Luo, “Fast Blood Flow Visualization of High-Resolution Laser Speckle Imaging Data Using Graphics Processing Unit,” Optics Express, Vol. 16, No. 19, 2008, pp. 14321-14329. doi:10.1364/OE.16.014321

[3]   J. D. Briers, “Optical Filtering Techniques to Enhance Speckle Contrast Variations in Single-Exposure Laser Speckle Photography,” Optik, Vol. 63, 1983, pp. 265-276.

[4]   J. D. Briers and S. Webster, “Laser Speckle Contrast Analysis (LASCA): A Non-Scanning, Full-Field Technique for Monitoring Capillary Blood Flow,” Journal of Biomedical Optics, Vol. 1, No. 2, 1996, pp. 174-179. doi:10.1117/12.231359

[5]   J. Ohtsubo and T. Asakura, “Velocity Measurement of a Diffuse Object by Using Time-Varying Speckles,” Optical and Quantum Electronics, Vol. 8, No. 6, 1976, pp. 523-529. doi:10.1007/BF00620143

[6]   A. Serov, W. Steenbergen and F. D. Mul, “Laser Doppler Perfusion with a Complimentary Metal Oxide Semiconductor Image Sensor,” Optics Letters, Vol. 27, No. 5, 2002, pp. 300-302. doi:10.1364/OL.27.000300

[7]   H. Y. Cheng, Q. M. Luo, S. Q. Zeng, et al., “Modified Laser Speckle Imaging Method with Improved Spatial Resolution,” Journal of Biomedical Optics, Vol. 8, No. 3, 2003, pp. 559-564. doi:10.1117/1.1578089

[8]   H.-M. Zhang, X.-P. Chen and Y.-J. Zhang, “Improved Wiener Filter Algorithm for Image Restoration,” Journal of Chongqing University of Technology (Natural Science), Vol. 24, No. 7, 2010, pp. 76-80.

[9]   S. M. Mansor Roomi, et al., “Impulse Noise Detection and Removal,” ICGST-GVIP Journal, Vol. 7, No. 2, 2007, pp. 51-56.

[10]   K. Somasundaram and P. Shanmugavadivu, “Impulsive Noise Detection by Second Order Differential Image and Noise Removal Using Nearest Neighborhood Filter,” International Journal of Electronics and Communications, Vol. 62, No. 6, 2007, pp. 472-477.

[11]   S. P. Ghael, A. M. Sayeed and R. G. Baraniuk, “Improved Wavelet Denoising via Empirical Wiener Filtering,” Proceedings of SPIE, San Diego, July 1997, pp. 389-399.

[12]   R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” 3rd Edition, Prentice Hall, Englewood Cliffs, 2008.