Gaussian Noise Removal in an Image using Fast Guided Filter and its Method Noise Thresholding in Medical Healthcare Application

Majeeth, S. Shaik; Babu, C. Nelson Kennedy

doi:10.1007/s10916-019-1376-4

Cited by 26 publications

(14 citation statements)

References 18 publications

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“…e results showed that the denoising effect (PSNR � 33.2762), structural similarity (SSIM � 0.8963), and edge similarity (FoM � 0.2975) were better than bilateral filtering and wavelet soft-threshold algorithms. It was consistent with the research results of Majeeth and Babu [11], showing that the combination of the wavelet transform algorithm and bilateral filtering algorithm can improve the quality of medical ultrasound images. Ultrasound is a noninvasive imaging technique that relies on the measurement of sound waves reflected in human tissues.…”

Section: Discussionsupporting

confidence: 91%

Wavelet Bilateral Filter Algorithm-Based High-Frequency Ultrasound Image Analysis on Effects of Skin Scar Repair

Xuan

Liu

et al. 2021

Scientific Programming

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It aimed to explore the application value of high-frequency ultrasound image based on the wavelet bilateral filtering algorithm in the repair evaluation of hypertrophic scar and keloid, as well as the expression change of nerve growth factor (NGF). 72 patients with scars admitted to hospital from October 2018 to October 2019 were chosen and classified as hypertrophic scar patients (group A) and keloid patients (group B), with 36 cases in each group. All patients received scar repair treatment, and another 30 normal skin volunteers (group C) were selected. High-frequency ultrasound images based on the wavelet bilateral filter algorithm were utilized for skin examination. The expression differences of NGF and pro-NGF in each group were measured by immunohistochemical staining and Western blot. The results showed that the denoising effect (signal-to-noise ratio (PSNR) = 33.2762), structural similarity (SSIM = 0.8963), and edge similarity (FoM = 0.2975) of the proposed algorithm were better than those of bilateral filtering and wavelet soft-threshold algorithms. The skin thickness of groups A and B was considerably higher relative to that of group C ( P < 0.05), but the echo intensity of the skin dermis of groups A and B was evidently inferior to that of group C ( P < 0.05). The results of immunohistochemistry showed that in contrast to group C, the average optical density of NGF in groups A and B increased remarkably ( P < 0.01). In contrast to group C, the positive expression of pro-NGF in groups A and B was notably reduced ( P < 0.05). Western blot results showed that relative to group C, the expression of NGF protein in groups A and B was increased greatly ( P < 0.01), but the expression of pro-NGF protein in groups A and B was reduced ( P < 0.01). The results suggested that the wavelet bilateral filter algorithm can be adopted to evaluate pathological skin scars, and pathological skin scar formation was closely related to the expression levels of NGF and pro-NGF.

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Section: Discussionsupporting

confidence: 91%

Wavelet Bilateral Filter Algorithm-Based High-Frequency Ultrasound Image Analysis on Effects of Skin Scar Repair

Xuan

Liu

et al. 2021

Scientific Programming

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“…where ∂ 2 is the variance of the Gaussian filter with 3 x 3 kernel size and x and y are the horizontal and vertical axes, respectively, of the kernel size [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation

et al. 2023

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Background The COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest x-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention. Objective We introduced a novel hybrid machine learning model to accurately classify COVID-19, non-COVID-19, and healthy patients from CXR images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared with existing models. Methods A retrospective study was conducted to analyze 5 public data sets containing 4200 CXR images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were preprocessed to undergo image segmentation, enhancement, and feature extraction. The best performing machine learning technique was selected and combined into a multilayer hybrid classification model for COVID-19 (MLHC-COVID-19). The model consisted of 2 layers. The first layer was designed to differentiate healthy individuals from infected patients, while the second layer aimed to classify COVID-19 and non-COVID-19 patients. Results The MLHC-COVID-19 model was trained and evaluated on unseen COVID-19 CXR images, achieving reasonably high accuracy and F measures of 0.962 and 0.962, respectively. These results show the effectiveness of the MLHC-COVID-19 in classifying COVID-19 CXR images, with improved accuracy and a reduction in interpretation time. The model was also embedded into a web-based MLHC-COVID-19 computer-aided diagnosis system, which was made publicly available. Conclusions The study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system.

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“…A local autonomous navigation method by applying the boundary line of tillage soil is presented in this paper, with the tractor works in tillage mode inspired by the existing plant line navigation method. Moreover, a fast image-processing algorithm using optimized templates combined with guided filtering [9,10,11,12], improved anti-noise morphology, and Hough transformation [13,14] is proposed to meet the practical application of agricultural tillage.…”

Section: Introductionmentioning

confidence: 99%

Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

Lü

Zeng

Wang

et al. 2019

Sensors

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An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094 s in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of 0.113 s . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of 140 × 260 pixels could achieve high-precision vision navigation while the course deviation angle was not more than 7.5 ° . The maximum tractor speed of the optimal template and global template were 51.41 km / h and 27.47 km / h , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.

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Gaussian Noise Removal in an Image using Fast Guided Filter and its Method Noise Thresholding in Medical Healthcare Application

Cited by 26 publications

References 18 publications

Wavelet Bilateral Filter Algorithm-Based High-Frequency Ultrasound Image Analysis on Effects of Skin Scar Repair

Wavelet Bilateral Filter Algorithm-Based High-Frequency Ultrasound Image Analysis on Effects of Skin Scar Repair

Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation

Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

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