Speckle Noise Reduction in Medical Ultrasound Images

Hiremath, P. S.; Akkasaligar, Prema T.; Badiger, Sharan

doi:10.5772/56519

Cited by 56 publications

(28 citation statements)

References 50 publications

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“…The generalized model of speckle noise is given in [21]. Commonly, the despeckling the image is too hard since dissimilarity resolution and the intensity of the noisy pixel varies with the image intensity [11]. Recently, the various methods are introduced to suppress noise and improve the quality of the US images and the same are briefly reviewed hereunder.…”

Section: Methodsmentioning

confidence: 99%

Despeckleing Prostate Ultrasonograms Using Pde With Wavelet

Ramesh¹,

Manavalan²

2018

IJIVP

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Prostate cancer is the leading cause of death for men, since the cause of the disease is mysterious and its early detection is also monotonous. Ultrasound (US) is the most popular tool to detect the human organ glands and also used to diagnose the prostate cancer. Speckle noise is an inherent nature of ultrasound images, which degrades the image quality. So far, No specific filter is available to suppress the speckle noise in prostate image. In this paper, a novel despeckling method PDE with Wavelet is presented for prostate US images. The enhancement method is evaluated by using standard measures like Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Edge Preservation Index (EPI). Further, the despeckling approaches' is also evaluated time and space complexity. From the results, it is observed that the filtering method PDE with Wavelet is superior to PDE in terms of denoising and also preserving the information content.

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Section: Methodsmentioning

confidence: 99%

Despeckleing Prostate Ultrasonograms Using Pde With Wavelet

Ramesh¹,

Manavalan²

2018

IJIVP

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“…, where MSE (Mean Square Error) is used to measure the quality change between the original image and enhanced image, it is computed by: The PSNR will be higher for a good quality image and lower for poor quality image. It measures image loyalty, that is, the manner by which nearly the changed images takes after the original image [2].…”

Section: Design and Analysismentioning

confidence: 99%

“…This work will be focusing on reduction of the different types of the commonly occurring artifacts in medical images. Also, it will improve the quality of an image [2]. This system will depend on an algorithms that reduce the artifacts from the medical image and then compare the new image with the original image to make sure that no significant loss of the fine details and more enhanced image is produced finally.…”

Section: Introductionmentioning

confidence: 99%

Reduction of Ultrasound Imaging Artifacts

Al-Saqri

2017

J Stud Res

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Nowadays, with the development of medicine and medical equipment, medical imaging has become an important part in health care sector. Doctors can diagnose diseases using medical imaging without making cut/wound in the human’s body; ultrasound is efficiently used because of its low cost and non-invasive nature and produces good quality images. However, artifacts are a common occurrence in an ultrasound display such as degraded, ambiguity, resolution etc. These artifacts affect the diagnosis accuracy. Thus, artifacts reduction in medical image is essential. In this paper, we describe and study medical image’s artifacts reduction techniques. Different image enhancement techniques for removing different types of artifacts without losing the fine details are studied to produce enhanced images. These reduction techniques are implemented using Matlab. The main objective of this project is to improve the quality of the medical images in order to help doctors to make better diagnosis decisions. This proposed work is expected to be very useful and efficient to use.

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“…However, the proposed denoising suppresses up to some extent all additive components regardless of their probability distribution. On the other hand, multiplicative speckle noise is generally more difficult to remove than additive noise, because the intensity of the noise varies with the image intensity [14]. Image noise is usually random, but ultrasound speckle is not random and results from some patterns of constructive and destructive interference shown as bright and dark dots in the image.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure

et al. 2020

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In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the direct recursive structures and recursive algorithms for the DCT/IDCT with Arbitrary Length. Experimental results on clinical ultrasound images and comparisons with classical Wiener filter, non-local mean (NLM) filter and total variation (TV) algorithms are used to validate the improvements of the proposed approaches in both noise reduction and reconstruction performance for ultrasound images.

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Speckle Noise Reduction in Medical Ultrasound Images

Cited by 56 publications

References 50 publications

Despeckleing Prostate Ultrasonograms Using Pde With Wavelet

Despeckleing Prostate Ultrasonograms Using Pde With Wavelet

Reduction of Ultrasound Imaging Artifacts

Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure

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