Iris identification using contourlet transform

Zewail, Rami; Mandal, Mrinal; Durdle, N.G.

doi:10.1117/12.703511

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Among these "beyond wavelet" techniques, contourlet allows for different and flexible number of directions at each scale, while achieving nearly critical sampling. The improvement in approximation by contourlets based on keeping the most significant coefficients will directly lead to improvements in applications [9][10][11][12][13][14][15][16][17], including denoising, compression, and feature extraction.…”

Section: Contourlet Transformmentioning

confidence: 99%

Speckle supression for medical ultrasound images based on modelling speckle with Rayleigh distribution in contourlet domain

Song

Zhang

Song

et al. 2008

2008 International Conference on Wavelet Analysis and Pattern Recognition

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In this paper, we proposed a new speckle suppression method for medical ultrasound images based on contourlet transform. Modeling the speckle with Rayleigh distribution in logarithmically transformed ultrasound images, a maximum a posterior (MAP) estimator is applied for speckle reduction via manipulating the coefficients in contourlet domain. Experimental results show that while effectively suppress the speckle, the proposed method has a better performance on preserving fine feature details in comparison with several traditional medical ultrasound denoising techniques.

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Section: Contourlet Transformmentioning

confidence: 99%

Speckle supression for medical ultrasound images based on modelling speckle with Rayleigh distribution in contourlet domain

Song

Zhang

Song

et al. 2008

2008 International Conference on Wavelet Analysis and Pattern Recognition

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show abstract

“…Capitalizing on this property, contourlet transform can be applied in a wide range of image processing tasks, such as denoising [9−12] , texture classification [13] , and fusion [14,15] . Furthermore, it has been applied in the field of synthetic aperture radar (SAR) image processing [10,15] , as well as medical image processing [16,17] and shows its potentials.…”

Section: Introductionmentioning

confidence: 99%

Speckle reduction based on contourlet transform using scale adaptive threshold for medical ultrasound image

Song

Chen

Zhang

et al. 2008

J. Shanghai Jiaotong Univ. (Sci.)

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A new speckle suppression method in contourlet domain was presented. By modeling the subband contourlet coefficients of the ultrasound images after logarithmic transform as generalized Gaussian distribution (GGD), we gave a scale-adaptive threshold in Bayesian framework. Experimental results of both synthetic and clinical ultrasound images show that our method has a better performance on speckle suppressing than the wavelet-based method while well preserving the feature details.

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Iris identification using contourlet transform

Cited by 2 publications

References 4 publications

Speckle supression for medical ultrasound images based on modelling speckle with Rayleigh distribution in contourlet domain

Speckle supression for medical ultrasound images based on modelling speckle with Rayleigh distribution in contourlet domain

Speckle reduction based on contourlet transform using scale adaptive threshold for medical ultrasound image

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