A survey of denoising techniques for multi-parametric prostate MRI

Garg, Gaurav; Juneja, Mamta

doi:10.1007/s11042-018-6487-2

Cited by 21 publications

(10 citation statements)

References 38 publications

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Section: Introductionsupporting

confidence: 55%

“…These processes greatly improve the image quality for diagnosis and staging of malignancies using advanced CAD techniques like segmentation and classification. Similar studies performed by researchers using image denoising and restoration to improve the image quality for segmentation and classification are given by Juneja et al, 6 Garg et al, 7 Thakur et al, 8 and Kaur et al 9 Complex imaging modality such as CT is one of the most comprehensive imaging tests used by clinicians to identify pancreatic cancer. This technique uses X-rays to get a detailed Three-dimensional (3D) image of the target organ.…”

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confidence: 64%

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Denoising of computed tomography using bilateral median based autoencoder network

Juneja

Joshi

Singla

et al. 2021

Int J Imaging Syst Tech

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Denoising of Computed tomography (CT) images is a critical aspect of image processing that is expected to improve the performance of Computer‐aided diagnosis (CAD) systems. However, the use of complex imaging modalities such as CT imaging to ascertain pancreatic cancer is vulnerable to gaussian and poisson noises, making image denoising an imperative step for the accurate performance of CAD systems. This paper presents a Bilateral median based autoencoder network (BMAuto‐Net) constructed with intermediate batch normalization layers and dropout factors to eliminate gaussian noise from the CT images. The skip connections adjoining the network, prevent performance degradation that generally occurs in most autoencoder architectures. Based on the presented study, BMAuto‐Net is reckoned to outperform other traditional filters and autoencoders. The performance measurement of the proposed architecture is performed using the peak signal‐to‐noise ratio (PSNR), mean squared error (MSE), and structured similarity index (SSIM) metric values. The Cancer imaging archive (TCIA) dataset consisting of 19 000 CT images is used to validate the performance of the architecture with average PSNR values of 30.01, 30.53, and 30.52, MSE values of 98.23, 98.87, and 98.94, and SSIM of values of 0.67, 0.60, and 0.57 for noise factors (NFs) of 0.1, 0.3, and 0.5 respectively.

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Section: Introductionsupporting

confidence: 55%

mentioning

confidence: 64%

Denoising of computed tomography using bilateral median based autoencoder network

Juneja

Joshi

Singla

et al. 2021

Int J Imaging Syst Tech

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“…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

Juneja

2020

Wireless Pers Commun

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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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“…The current clinical methods for diagnosing CRLM mainly include abdominal enhanced CT, serum CEA, pathological staging, and liver MRI examinations [9,[20][21][22][23][24]. Because there were certain differences in each person's genes, doctors needed to analyze and detect whether there were mutations in their patients' related oncogenes and then conduct personalized targeted therapy.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis of Gene and Radiomic Features in Colorectal Cancer Liver Metastases

Wang

Guo

et al. 2022

Computational and Mathematical Methods in Medicine

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Colorectal cancer liver metastasis (CRLM) was one of the cancers with high mortality. Clinically, the target point was determined by invasive detection, which increased the suffering of patients and the cost of treatment. If the target point was found through the relationship between early radiomic information and genetic information, it was expected to assist doctors in diagnosing disease, formulating treatment plans, and reducing the pain and burden of patients. In this study, gene coexpression analysis and hub gene mining were first performed on the gene data; secondly, quantitative radiomic features were extracted from CT-enhanced radiomic data to obtain features highly correlated with CRLM; and finally, we analyzed the relationship between gene features and radiomic feature correlations by establishing a link between early radiomic features and gene sequencing and finding highly correlated expressions. This experiment demonstrated that radiomic features could be used to mine gene attributes. Based on the four previously identified genes (NRAS, KRAS, BRAF, and PIK3CA), we identified two novel genes, MAPK1 and STAT1, highly associated with CRLM. There were specific correlations between these 6 genes and radiomic features (shape_elongation, glcm, glszm, firstorder_10percentile, gradient, exponent_firstorder_Range, and gradient_glszm_SmallAreaLowGrayLevel). Therefore, this paper established the correlation between radiomic features and genes, and through radiomic features, we could find the genes associated with them, which was expected to achieve noninvasive prediction of liver metastasis.

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A survey of denoising techniques for multi-parametric prostate MRI

Cited by 21 publications

References 38 publications

Denoising of computed tomography using bilateral median based autoencoder network

Denoising of computed tomography using bilateral median based autoencoder network

Pre-processing of Retinal Images for Removal of Outliers

Correlation Analysis of Gene and Radiomic Features in Colorectal Cancer Liver Metastases

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