Image Denoising using Contourlet Transform

Sivakumar, R.; Balaji, G.; Ravikiran, R.S.J.; Karikalan, R.; Janaki, S.

doi:10.1109/iccee.2009.70

Cited by 18 publications

(6 citation statements)

References 4 publications

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“…The Laplacian Pyramid at each level generates a Low pass output (LL) and a Band pass output (LH, HL, and HH). The Band pass output is then passed into Directional Filter Bank, which results in contourlet coefficients [15]. The Low pass output is again passed through the Laplacian Pyramid [1] to obtain more coefficients and this is done till the fme details of the image are obtained.…”

Section: Methodsmentioning

confidence: 99%

Skull removal of noisy magnetic resonance brain images using Contourlet transform and morphological operations

Satheesh

Kumar

Prasad³

et al. 2011

Proceedings of 2011 International Conference on Computer Science and Network Technology

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Efficient segmentation of noisy Magnetic Resonance( MR) brain images is a challenging task, as pre and post surgery decisions are required to make accurately in achieving better medical practices while treating brain disorders. This paper presents an automatic segmentation technique to remove non brain tissue (skull, fat, skin, muscle) of noisy MR brain images and to extract brain tissue (cortex and cerebellum). Here, Contourlet transform is applied to denoise a noisy MR brain image and threshold based morphological operations are applied to extract brain region on denoised images. Hence a comparative study is developed on skull removed MR brain images with and without denoising based on similarity index and segmentation error. The experimental results prove that the proposed method yields consistent results irrespective of noise levels.

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Section: Methodsmentioning

confidence: 99%

Skull removal of noisy magnetic resonance brain images using Contourlet transform and morphological operations

Satheesh

Kumar

Prasad³

et al. 2011

Proceedings of 2011 International Conference on Computer Science and Network Technology

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show abstract

“…The Laplacian Pyramid at each level generates a Low pass output (LL) and a Band pass output (LH, HL, and HH). The Band pass output is then passed into Directional Filter Bank, which results in contourlet coefficients [8]. The Low pass output is again passed through the Laplacian Pyramid [7] to obtain more coefficients and this is done till the fine details of the image are obtained.…”

Section: Contourlet Transformmentioning

confidence: 99%

Medical Image Denoising Using Adaptive Threshold Based on Contourlet Transform

Satheesh¹,

Prasad²

2011

ACIJ

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“…Computational complexity has increased because of spatial operation. Further, improvement in perceptual quality of an image can also be achieved by proper shrinkages using an optimum threshold value determined by sub-band adaptive method based on WT, discrete cosine transform (DCT), partial differential equations, contourlet transform, undecimated DWT [22][23][24][25][26][27][28][29][30][31]. Nasri and Pour [28]h a v e introduced the adaptive thresholding function using WT-based thresholding neural network (WT-TNN) methodology in 2009.…”

Section: Introductionmentioning

confidence: 99%

Improved sub‐band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms

Soni

Bhandari

Kumar

et al. 2013

IET signal process.

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In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the CS algorithm and ABC algorithm-based denoising approach give better performance in terms of edge preservation index or edge keeping index (EPI or EKI) peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) as compared to PSO-based denoising approach. The proposed technique has been tested on satellite images. The quantitative (EPI, PSNR and SNR) and visual (denoised images) results show superiority of the proposed technique over conventional and state-of-the-art image denoising techniques.

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Image Denoising using Contourlet Transform

Cited by 18 publications

References 4 publications

Skull removal of noisy magnetic resonance brain images using Contourlet transform and morphological operations

Skull removal of noisy magnetic resonance brain images using Contourlet transform and morphological operations

Medical Image Denoising Using Adaptive Threshold Based on Contourlet Transform

Improved sub‐band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms

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