A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms

Malar, E.; Kandaswamy, A.; Kirthana, S.; Nivedhitha, D.

doi:10.1109/mvip.2012.6428762

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…Thereafter, we compare the experimentally obtained decreasing order of average PSNR i.e., PSNR wavelet (35.83 db) > PSNR contourlet (35.65 db) > PSNR curvelet (34.68) with that given by Eq. 1 (second row, Table 1, [7]) and find that our investigated order is correct. In the quest of improving PSNR, we implement the PURE-LET to suppress noise from the same mammograms and find that it removes noise better than the curvelet, contourlet and wavelet transforms.…”

Section: Resultsmentioning

confidence: 57%

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Poisson Noise Removal from Mammogram Using Poisson Unbiased Risk Estimation Technique

Saha

Naskar

Chatterji³

2015

Advances in Intelligent Systems and Computing

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We present an experimental work on the denoising of mammogram with Poisson noise. Reviewing the literature, it is found that the denoising performance of the multiresolution tools like wavelet, contourlet and curvelet implemented on mammogram with Poisson noise is unique. The first part of the investigation deals with the confirmation of this exceptional performance with our result. The later half implements the recently developed denoising approach called the Poisson Unbiased Risk Estimation-Linear Expansion of Thresholds (PURE-LET) to the Poisson noise corrupted mammogram with an objective to improve the peak signal to noise ratio (PSNR) further. The PURE-LET successfully removes Poisson noise better than the traditional mathematical transforms already mentioned. The computation time and PSNR are also evaluated in the perspective of the cycle spinning technique. This validates the applicability and efficiency of the novel denoising strategy in the field of digital mammography.

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Section: Resultsmentioning

confidence: 57%

“…The curvelet transform is also used by Malar et al [6] to reduce the noise from the mammograms. Malar et al [7] Therefore, the verification of this exceptional decreasing order of SNR for Poisson noise and subsequent effort to improve the same is the motivation of the current work.…”

Section: Introductionmentioning

confidence: 78%

Poisson Noise Removal from Mammogram Using Poisson Unbiased Risk Estimation Technique

Saha

Naskar

Chatterji³

2015

Advances in Intelligent Systems and Computing

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“…Ma and Plonka [18], presented a review on CT including its evolution history, theory and its correlation to other multiresolution methods. CT is implemented on a variety of medical images for segmenting and denoising and had efficient results [19,20]. Raju and Kumar [21] also compared denoising performances of dual tree complex WT and CT on medical images and CT had better results.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoising

Ceylan

Canbilen

2017

ijisae

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Abstract:Noise reduces the quality of medical images and raise the difficulties of diagnosis. Although the wavelet transform has already been used in medical noise removal applications extensively, there are many other multi-resolution analysis methods proposed in recent years for denoising. The main goal of this study is comparing the image denoising abilities of some of these methods with wavelet transform. In this paper, image denoising is implemented by a three-stage methodology. Effectiveness of the multiresolution analysis methodologies has been investigated for standard test images beside magnetic resonans, mammography and fundus images. Performances of the transforms are compared by using peak signal to noise ratio, mean square error, mean structural similarity index and feature similarity index. The best results are obtained by tetrolet transform for random and rician noises with the benchmark images. Medical image denoising performance of Tetrolet transform is compared to other multi resolution analysis methods for the first time in the literature with this study. It surpassed ridgelet and haar wavelet transforms while the noise ratio was low. On the other hand, it is seen that curvelet transforms are effectively produce the best results for all rates of noise on medical images.

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“…It helps to screen the breast cancer by pointing out the microcalcifications and other clinically hidden lumps of breast tissues. But this process of medical image acquisition is often affected by several types of noise like Salt and Pepper, Poisson, Gaussian, Speckle and demands subsequent noise filtration for the effective feature extraction [2]. So far there has been numerous works on mammogram denoising using the multiresolution mathematical transforms called the wavelet, curvelet and contourlet transforms.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed technique when compared with the conventional undecimated discrete wavelet transform exposes less computation time and better image quality. A comparative study on mammogram denoising by the three transforms called the wavelet, contourlet and curvelet reveals that the curvelet transform outperforms the other tools for Salt & Pepper, Gaussian and Speckle noises [2]. Eltoukhy et al also compared the wavelet transform with the curvelet transform for the detection of breast cancer and statistically established the superiority of the curvelet transform [7].…”

Section: Introductionmentioning

confidence: 99%

Mammogram denoising by curvelet transform based on the information of neighbouring coefficients

Saha

Naskar

Chatterji

2015

Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)

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We present here an experimental work on mammogram denoising by the mathematical tool called the curvelet transform. The infiltration of noise in mammogram during the X-ray screening is a common and inevitable phenomenon. And such noise is normally reduced by the curvelet transform based on conventional thresholding strategy called the hard thresholding (HT). Therefore, the motive of this experimentation is to suggest an alternate but efficient mechanism of mammogram denoising by the same transform but with different algorithms purely based on the information of the neighbouring coefficients. It is found that the curvelet transform applied with the precept of surrounding curvelet coefficients is visually and statistically better than the conventional approach based on HT.

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A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms

Cited by 8 publications

References 13 publications

Poisson Noise Removal from Mammogram Using Poisson Unbiased Risk Estimation Technique

Poisson Noise Removal from Mammogram Using Poisson Unbiased Risk Estimation Technique

Performance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoising

Mammogram denoising by curvelet transform based on the information of neighbouring coefficients

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