Medical Image Denoising Techniques: A Review

Patil, Rashmi S.; Bhosale, Surendra

doi:10.46328/ijonest.76

Cited by 17 publications

(11 citation statements)

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“…Thresholding techniques had to be used before applying Bilateral and NLM filters to preserve edges and details. Comparing all the conventional methods for medical images [7], it is evident that a particular way performs better on a specific type of noise only.…”

Section: Related Workmentioning

confidence: 99%

“…CNN architecture may move away from traditional methodologies and toward deep learning methods, yet, the significant difficulty remains computational time and space. TV (Total Variation) based regularization methods [7] have also been proposed for denoising, and these are useful in solving the issue of smoothness, but they have drawbacks. Flat areas are approximated by a fixed constant resulting in a staircase effect and sometimes loss of contrast [9].…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned before, denoising images generate blurriness and can remove essential details from the images, which is very harmful in the case of medical imaging. Hence, it's necessary to perform some preprocessing or post-processing on images before or after performing the denoising [7] [12]. HDR (High Dynamic Range) is a technique that can be used on X-ray medical images to enhance and improve edge details [12].…”

Section: Related Workmentioning

confidence: 99%

“…In natural images, this might work to some extent. However, for medical images, this is highly problematic because edges contain many essential details that are useful in disease detection and general inference [7]. In our denoising case, the blurring out of the edge, although much less than traditional methods, still exists.…”

Section: The Pipelinementioning

confidence: 99%

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An End-to-End Pipeline for Medical Image Enhancement using GANs Architecture

Patel¹,

Singh²,

Dankar³

2023

Preprint

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Medical Imaging is used by radiologists for diagnostic purposes and to check for abnormalities, and these imaging techniques involve radiation. Overexposure to radiation can have an adverse impact on the human body, and using less radiation gives us a noisy output. Hence, radiologists find it difficult as there is a trade-off between the amount of radiation that can be used and the quality of the image. Moreover, noise in medical images can occur due to fluctuation of photons, a reflection of radiations from the subject, or due to instrumental vibration or faults. The proposed approach is a pipeline which starts with denoising using GANs architecture, in which two models have been trained, one for handling all kinds of noise and the second one specifically for Poisson noise. Further, post-processing methods like single-shot HDR using Retinex Filtering and Edge Enhancement using unsharp masking have been done to get a structurally more similar and enhanced denoised image.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Pipelinementioning

confidence: 99%

See 2 more Smart Citations

An End-to-End Pipeline for Medical Image Enhancement using GANs Architecture

Patel¹,

Singh²,

Dankar³

2023

Preprint

View full text Add to dashboard Cite

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“…The usage of the ridgelet transform is therefore omitted to boost the speed of the transform and decrease its redundancy. In general, all curvelet transforms can be classified into one of the three groups: 28 a. The magnitude equals zero when the discontinuities and lengthwise supports do not intersect.…”

Section: Continuous Curvelet Transformmentioning

confidence: 99%

(Retracted) Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Abbas

2022

J. Electron. Imag.

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Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space.

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A user independent denoising method for x‐nuclei MRI and MRS

Christensen

Væggemose

Bøgh

et al. 2023

Magnetic Resonance in Med

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PurposeX‐nuclei (also called non‐proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x‐nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state‐of‐the‐art denoising method.MethodsDenoising approaches were compared on human acquisitions of sodium (23Na) brain, deuterium (2H) brain, carbon (13C) heart and brain, and simulated dynamic hyperpolarized 13C brain scans, with and without additional noise. The current state‐of‐the‐art denoising method Global‐local higher order singular value decomposition (GL‐HOSVD) was compared to the few‐input method tensor Marchenko‐Pastur principal component analysis (tMPPCA). Noise‐removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean‐square‐error and Bland–Altman analysis to quantify agreement between original and denoised results of noise‐added data.ResultsGL‐HOSVD and tMPPCA showed similar performance for the variety of x‐nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL‐HOSVD. The mean ratio between noise‐added and denoising reproducibility coefficients of the Bland–Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL‐HOSVD and tMPPCA, respectively.ConclusionThe strength of tMPPCA lies in the few‐input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x‐nuclei imaging improvements and the preferred denoising method.

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Medical Image Denoising Techniques: A Review

Cited by 17 publications

References 0 publications

An End-to-End Pipeline for Medical Image Enhancement using GANs Architecture

An End-to-End Pipeline for Medical Image Enhancement using GANs Architecture

(Retracted) Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

A user independent denoising method for x‐nuclei MRI and MRS

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