Noise Removal for Medical X-ray Images in Wavelet Domain

Lü, Jianming; Li, Yeqiu; Yahagi, Takashi; Okamoto, Takahide

doi:10.1541/ieejeiss.126.237

Cited by 11 publications

(11 citation statements)

References 7 publications

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“…X-rays have Poisson noise. Thus, preprocessing [7,8,9,10,11] is required for noise removal. There are various methods to remove noise and blurriness and make objects more prominent.…”

Section: A Removal Of Poisson Noisementioning

confidence: 99%

Evaluation of tumor segmentation

Bansal

Mittal

2013

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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X-ray images are commonly used for diagnosis because of the property that different tissues absorb X-rays differently. Detection of lytic areas using X-ray images is a challenging problem due to irregular shapes, intensity inhomogeneity and diffused boundaries. The problem has been addressed by segmenting lytic area on the basis of different cues such as intensity, density, and texture. However, the approaches based on single cue have failed to produce the desired results. In this paper, a hybrid technique has been tested to segment lytic region using X-ray images. The hybrid method has been qualitatively evaluated by taking assistance from the domain experts. The results of evaluation indicate that the hybrid approach shows better result as compared to single cue techniques.

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“…X-rays have Poisson noise. Thus, preprocessing [7,8,9,10,11] is required for noise removal. There are various methods to remove noise and blurriness and make objects more prominent.…”

Section: A Removal Of Poisson Noisementioning

confidence: 99%

Evaluation of tumor segmentation

Bansal

Mittal

2013

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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“…They modity the weights of median filter window to make it directional helping to remove high amplitude of noise from X-ray image compare to Bayes shrink technique which removes low amplitude noise. Same author in [6] put forward a denoising technique for degraded medical images using 978-1-4799-7165-7/15/$31.00 ©2015 IEEE 568 improved Bayes shrink technique and uses Daubechies wavelet transform for decomposition. Author makes use of different dose X-ray images in his experimentation.…”

Section: Introductionmentioning

confidence: 99%

“…Apply soft threshold to that corner points only. 6 Proposed method is implemented and compared with different papers with same images used in them. Following are the comparison shown with different methods.…”

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confidence: 99%

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Poisson noise reduction from X-ray medical images using modified Harris operator and wavelet domain thresholding

Thakur¹,

Kadam²,

Ambhore³

et al. 2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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X-rays with photon having wavelength below 0.2-0.1 nm are generally used to produce X-ray images due to their high penetration ability. But photon counting statistics follows Poisson noise distribution which degrades quality of medical data (X-ray)represented by photon images. This Poisson noise can be reduced by increasing the dose of X-ray for production of X-ray image but it will harm patient body. In this paper, modified Harris operator along with wavelet domain thresholding is proposed for X-ray image denoising. Harris operator finds the pixels in image having more intensity variation compare to neighboring pixels, such pixels can be called as noisy pixels. We compare our denoised results with other denoising techniques and found significant improvement in result. IndexTerms-X-ray image, Poisson noise, waveletThresholding, Harris operator.

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“…However, if we reduce the current of an x-ray tube, the quality of images is degraded by Poisson noise. In the field of image processing, several methods with a discrete wavelet transform have been proposed to remove the noise [1]- [3]. Donoho proposed a method to reduce noise by applying a soft thresholding to the wavelet coefficients [1].…”

Section: Introductionmentioning

confidence: 99%

Noise reduction and contrast enhancement for small-dose X-ray images in wavelet domain

Sakata

Ogawa

2009

2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC)

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An important issue in diagnostic imaging is to acquire high quality images with a small dose. However x-ray images acquired with a small dose are affected by the quantum noise. In this paper, we propose a new method to yield high quality images acquired with a small dose by reducing the noise and enhancing the contrast of images adequately. The proposed method consisted of the following steps: (1) pre-processing of an original image, (2) making 16 images with shifting the position of all pixels by several pixels in the image, (3) decomposition of these 16 images with a wavelet transform and reducing Poisson noise with a modified BayesShrink method in the wavelet domain, (4) reconstructing 16 denoised images with an inverse wavelet transform and restoring the pixel position, (5) averaging of 16 images pixel by pixel in the spatial domain, (6) decomposition of the averaged image with the wavelet transform and enhancing the contrast in the wavelet domain, (7) reconstructing a denoised and contrast enhanced image with the inverse wavelet transform, and (8) post-processing of the obtained image. To confirm the validity of our proposed method we acquired x-ray images of a standard human body phantom with small doses of 1/12 and 1/25 of the standard dose, and applied our method to these low-dose xray images. The validity of the proposed method was confirmed in comparison with the results processed using conventional methods.

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Noise Removal for Medical X-ray Images in Wavelet Domain

Cited by 11 publications

References 7 publications

Evaluation of tumor segmentation

Evaluation of tumor segmentation

Poisson noise reduction from X-ray medical images using modified Harris operator and wavelet domain thresholding

Noise reduction and contrast enhancement for small-dose X-ray images in wavelet domain

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