Adaptive multi-threshold based de-noising filter for medical image applications

Ramya, A.; Murugan, D.; Murugeswari, G.; Joseph, Nisha

doi:10.1504/ijcvr.2019.099439

Cited by 3 publications

(2 citation statements)

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“…The edge-preserving median filter (EPM), which controls the blurring effect while keeping the fine details of the interior region, is used to modify the noisy pixel in the second phase. With benchmark images and medical images, the planned work is evaluated [9]. Additionally, the fruit fly individual is tested by the HACLFOA algorithm not only as a total item but also as an individual in each dimension during the optimizing process, which increases the solving accuracy of the algorithm [10].…”

Section: A Ahilan Et Al ( 2019) Said Thatmentioning

confidence: 99%

Optimization Methods for Image Thresholding: A review

Abd,

Hussein Alrawi,

Bassel

2023

JUAPS

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Setting a border with the proper gray level in processing images to separate objects from their backgrounds is crucial. One of the simplest and most popular methods of segmenting pictures is histogram-based thresholding. Thresholding is a common technique for image segmentation because of its simplicity. Thresholding is used to separate the Background of the image from the Foreground. There are many methods of thresholding. This paper aims to review many previous studies and mention the types of thresholding. It includes two types: the global and local thresholding methods and each type include a group of methods. The global thresholding method includes (the Otsu method, Kapur's entropy method, Tsallis entropy method, Hysteresis method, and Fuzzy entropy method), and the local thresholding method includes ( Ni-Black method and Bernsen method). The optimization algorithms(Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Modified Grasshopper Optimization, Firefly Algorithm, Cuckoo Search, Tabu Search Algorithm, Simulated Annealing, and Jaya Algorithm) used along with thresholding methods are also illustrated.

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Section: A Ahilan Et Al ( 2019) Said Thatmentioning

confidence: 99%

Optimization Methods for Image Thresholding: A review

Abd,

Hussein Alrawi,

Bassel

2023

JUAPS

View full text Add to dashboard Cite

show abstract

“…The trained model which had the best performance (i.e., with the highest Dice score) for the test dataset was kept. Training each instance of the network took approximately 6 h. In the postprocessing stage, region growing (29) and two-threshold refinement (30) were used for further optimization of the segmentation results. The network loss function was based on the concept of the similarity between the output image and the ground-truth image.…”

Section: Quantification Of Ccta-cac Score On Ccta Scansmentioning

confidence: 99%

An automated quantification method for the Agatston coronary artery calcium score on coronary computed tomography angiography

Wang¹,

Yang²,

Wang³

et al. 2022

Quant Imaging Med Surg

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Background: A coronary artery calcium (CAC) score can provide supplementary information for predicting the risk of cardiovascular disease (CVD). Although CAC is clinically measured with non-contrast cardiac computed tomography (CT), coronary CT angiography (CCTA) may also be used, allowing for the simultaneous evaluation of coronary artery vessels and calcified plaques. This study proposes a method for the automated quantification of the Agatston CAC score from CCTA and compares our method's performance with that of non-contrast cardiac CT.Methods: Sixty-two patients were selected from a clinical registry and divided into four CAC categories. They underwent both non-contrast cardiac CT and CCTA. The Agatston CAC score derived from noncontrast cardiac CT (standard Agatston CAC score) was used as the reference standard. Calcifications were automatically identified and quantified using different thresholds after a deep learning-based coronary artery segmentation model pretrained on CCTA images. Comparisons were made between the standard Agatston CAC score and the CCTA-based Agatston CAC score (CCTA-CAC score) on a per-patient and per-vessel basis. Spearman's rank-order correlation coefficient (R) and intra-class correlation (ICC) values were used to calculate the correlation between the two methods.Results: After comparison, the optimal lower threshold in CCTA-CAC score calculations was found to be 650 Hounsfield units (HU). Using this threshold on a per-patient basis, the automatically computed CCTA-CAC score showed a high correlation (R =0.959; P<0.01) and ICC (R =0.8219; P<0.01) with the standard Agatston CAC score. On a per-vessel basis, the standard Agatston CAC score was also highly correlated with the CCTA-CAC score (R =0.889; P<0.01 and ICC =0.717; P<0.01). Of the 62 patients enrolled, 47 (76%)were classified into the same cardiovascular risk category using the CCTA-CAC score quantification method as when the standard Agatston CAC score was used. Agreement within the CAC categories was also good (kappa =0.7560).Conclusions: Fully automated quantification of the Agatston CAC score on CCTA images is feasible and shows a high correlation with the reference standard. This method could simplify the quantification procedure and has the potential to reduce the radiation dose and save time by eliminating the non-contrast cardiac CT stage.

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Efficient image denoising technique using the meshless method: Investigation of operator splitting RBF collocation method for two anisotropic diffusion-based PDEs

Lotfi

Parand

2022

Computers & Mathematics with Applications

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Adaptive multi-threshold based de-noising filter for medical image applications

Cited by 3 publications

References 0 publications

Optimization Methods for Image Thresholding: A review

Optimization Methods for Image Thresholding: A review

An automated quantification method for the Agatston coronary artery calcium score on coronary computed tomography angiography

Efficient image denoising technique using the meshless method: Investigation of operator splitting RBF collocation method for two anisotropic diffusion-based PDEs

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