A comprehensive review of denoising techniques for abdominal CT images

Kaur, Ravinder; Juneja, Mamta; Mandal, Arup Kumar

doi:10.1007/s11042-017-5500-5

“…The pre-processing of medical image is the preliminary step for a diagnosis purpose as the accuracy of further steps such as segmentation and classification are dependent on this. According to the literature different types of preprocessing approaches have been proposed by different researchers for removal of outliers from different imaging modalities such as Computed tomography (CT) [4] and Magnetic Resonance imaging (MRI) [5]. Diagnosis of glaucoma using CAD requires segmentation of optic disc and optic cup, the performance of which is dependent on the pre-processing of retinal fundus image, as better is the pre-processing improved is the segmentation and efficient is the diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Pre-processing of Retinal Images for Removal of Outliers

Thakur

¹

,

Juneja

²

2020

Wireless Pers Commun

Self Cite

4

1

0

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Early diagnosis of diseases related with retina such as glaucoma is of utmost importance in current scenario as it is the second most prevailing cause of irreversible blindness over the world and is expected to increase further in near future. It is commonly diagnosed using retinal images which are acquired by digital fundus cameras. But the acquired images may be prone to certain outliers that create hindrance in diagnosis of glaucoma by tempering the accuracy. These outliers include retinal vessels, low contrast of images and uneven illumination that deteriorates the performance of disc and cup segmentation which are the key indicators to diagnose glaucoma. Thus, pre-processing of retinal images to remove outliers plays a significant role in diagnosis. This paper presents an approach for pre-processing the retinal fundus image followed by its comparison with state of the art. Based on the experimental analysis the performance of the proposed approach is found to be better than the state of the art based on the analysis using metrics such as peak signal to ratio, mean square error and structural similarity index. Further, the proposed approach has been compared with state of the art using metrics such as Jaccard index and dice similarity on the basis of segmentation outcomes on different pre-processing approaches.

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“…As one of the basic parameters of image quality, SNR is an important criterion to evaluate medical image quality [ 25 , 26 ]. Previous studies have shown that high b value images usually tend to have a decrease in SNR due to the application of large diffusion gradients [ 6 ].…”

Section: Discussionmentioning

confidence: 99%

Effect of $b$ Value on Imaging Quality for Diffusion Tensor Imaging of the Spinal Cord at Ultrahigh Field Strength

Bao

¹

,

Zhao

²

,

Bao

³

et al. 2021

BioMed Research International

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Objective. To explore the optimal b value setting for diffusion tensor imaging of rats’ spinal cord at ultrahigh field strength (7 T). Methods. Spinal cord diffusion tensor imaging data were collected from 14 rats (5 healthy, 9 spinal cord injured) with a series of b values (200, 300, 400, 500, 600, 700, 800, 900, and 1000 s/mm2) under the condition that other scanning parameters were consistent. The image quality (including image signal-to-noise ratio and image distortion degree) and data quality (i.e., the stability and consistency of the DTI-derived parameters, referred to as data stability and data consistency) were quantitatively evaluated. The min-max normalization method was used to process the calculation results of the four indicators. Finally, the image and data quality under each b value were synthesized to determine the optimal b value. Results. b = 200 s / m m 2 and b = 900 s / m m 2 ranked in the top two of the comprehensive evaluation, with the best image quality at b = 200 s / m m 2 and the best data quality at b = 900 s / m m 2 . Conclusion. Considering the shortcomings of the ability of low b values to reflect the microstructure, b = 900 s / m m 2 can be used as the optimal b value for 7 T spinal cord diffusion tensor scanning.

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“…Computed Tomography (CT) is one of the reliable and non-invasive medical image modalities that help to detect pathological abnormalities in the human body such as tumors, vascular diseases, lung nodules, internal injuries, and bone fractures. In addition to the diagnostic support, CT is also useful in guiding various clinical procedures, including interventions, radiation therapies, and surgeries [ 38 ]. However, repeated CT scans may reveal that the patient may be exposed to radiation enormously.…”

Section: Introductionmentioning

confidence: 99%

“…These visual degradation cause blurring of the edges and losses of contrast within the organs and textures [ 11 ]. As a result, the reliability of both the clinical diagnostic procedures and automated analysis tasks such as segmentation, feature extraction, and classification of these LDCT images are deteriorated [ 38 ]. However, to overcome these visual degradations and improve the clinical usability of the LDCT images, there are various denoising algorithms have been proposed over the past 5 decades.…”

Section: Introductionmentioning

confidence: 99%

“…Our survey of relevant works has revealed that very few reviews have recently been published to discuss the conventional general CT denoising methods [ 11 , 38 ]. With the emergence of DL techniques, most of these conventional denoising algorithms discussed in those reviews are technically obsolete concerning the several LDCT restoration aspects such as accuracy of noise reduction, the ability of lesion discrimination, and the preservation of the fine structure and texture details.…”

Section: Introductionmentioning

confidence: 99%

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A review on Deep Learning approaches for low-dose Computed Tomography restoration

Kulathilake

¹

,

Abdullah

²

,

Sabri

³

et al. 2021

Complex Intell. Syst.

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Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.

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A comprehensive review of denoising techniques for abdominal CT images

Cited by 36 publications

References 57 publications

Pre-processing of Retinal Images for Removal of Outliers

Pre-processing of Retinal Images for Removal of Outliers

Effect of $b$ Value on Imaging Quality for Diffusion Tensor Imaging of the Spinal Cord at Ultrahigh Field Strength

A review on Deep Learning approaches for low-dose Computed Tomography restoration

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A comprehensive review of denoising techniques for abdominal CT images

Cited by 36 publications

References 57 publications

Pre-processing of Retinal Images for Removal of Outliers

Pre-processing of Retinal Images for Removal of Outliers

Effect of b Value on Imaging Quality for Diffusion Tensor Imaging of the Spinal Cord at Ultrahigh Field Strength

A review on Deep Learning approaches for low-dose Computed Tomography restoration

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Effect of $b$ Value on Imaging Quality for Diffusion Tensor Imaging of the Spinal Cord at Ultrahigh Field Strength