Segmentation and denoising of phase contrast MRI image of the aortic lumen via fractal and morphological processing

Rudnitskii, A. G.; Rudnytska, M. A.

doi:10.1109/elnano.2017.7939755

Cited by 4 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AD can be attributed to the growing rise in obesity and food-linked diseases (Cottica et al, 2019;Maia et al, 2020). AD is common in middle-aged and elderly men, presents often with sudden severe chest pain, radiating to the back, abdomen, and sometimes even with shock (Rudnitskii & Rudnytska, 2017). At present, there are many articles on the diagnosis of AD by CT and MRI (Nienaber et al, 1991;Melissano et al, 2012;Noorani et al, 2015;de Beaufort et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Comparison of CT and MRI in imaging diagnosis of aortic dissection

et al. 2022

View full text Add to dashboard Cite

Aortic dissection(AD) is a life-threatening disease due to a tear in the intimal layer of the aorta within the aortic wall. To compare diagnostic value and imaging of AD between computed tomography(CT) and magnetic resonance imaging (MRI). 120 AD patients diagnosed were examined with 64-slice CT and 1.5 MRI, the imaging data of true and false lumen, intimal flap, intimal tear, mural thrombus and aortic calcification were compared. The intimal flap rate of CT and MRI was 81.7% and 100%, respectively; The intimal tear rate of CT and MRI was 68.3% and 83.3%, respectively; The rate of mural thrombus in CT and MRI was 26.7% and 54.2%, respectively; The rate of aortic calcification in CT and MRI was 62.5% and 18.3%, respectively; The number of patients with intimal tear lower than 1mm in CT and MRI was 5 and 0, respectively. Both CT and MRI can show the true and false lumen well, but the detection rate of intimal flap, intimal tear and mural thrombosis in MRI is significantly higher than that in CT, and the detection rate of aortic calcification and intimal tear<1mm in CT is higher than that in MRI.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of CT and MRI in imaging diagnosis of aortic dissection

et al. 2022

View full text Add to dashboard Cite

show abstract

“…An asphalt pavement image generates a lot of random noises in the binarization due to its curved surface. Since it is hard to process a noise only with binarization, Median filtering [11,12] is performed. Median filter as a nonlinear filter is used to remove a random noise by keeping a border.…”

Section: Figure 4 Psnr and Mse Score 32 Threshold Based Pothole Region Extractionmentioning

confidence: 99%

Pothole Region Extraction Based On Similarity Evaluation Scale Classification Using Image Processing

et.al¹

2021

TURCOMAT

View full text Add to dashboard Cite

Pavement deterioration and abnormal climate induced by global warming lead to a constant rise in the number of potholes. Accordingly, the loss cost for maintenance and accidents also increases. Therefore, it is necessary to develop a method of classifying pavement potholes and detecting their locations. This study proposes the pothole region extraction based on similarity evaluation scale classification using image processing. The proposed technique sets up a classification threshold appropriately by considering the structure, brightness, and other factors of the grayscale-converted image through SSIM (Structural Similarity Index Measure). It binarizes porthole images classified according to the threshold, and then extracts pothole regions through the threshold based segmentation. A conventional image classification method utilizes the rules found in objects or the label selected by a user. The proposed method can take into account detailed factors by comparing image similarity in the unit of pixel. According to the performance evaluation, the proposed classification method’s F1-score is 0.83, and its accuracy of pothole region extraction is 0.851. Therefore, with the proposed technique, it is possible to make classification in consideration of similarity between images. In addition, the proposed method makes it possible to detect the regions similar to actual potholes accurately.

show abstract

“…This type of noise must be removed before the segmentation step. There are many types of algorithms are being developed to address the problem of speckle [9,10] This paper presents a new framework for the detection of breast tumors from ultrasound images that have speckle noise. The proposed framework contains a set of stages such as image enhancement, image segmentation, and classification.…”

Section: Introductionmentioning

confidence: 99%

“…) ln pF (m)(9) En = EnT + EnI + EnF(10) The next pseudocode shows the process of removing speckle noise from the breast ultrasound using the next pseudocodePseudocodeNeutrosopic Filter for denoisong Input: Noisy Ultrasound image Output: grayscale image 1-Transform noisy image to True, intermediacy and false set using neutrosophic 2-Calculate entropy for intermediacy set 3-Apply neutrosophic filter on True set to obtain T' 4-Check stopping criteria by comparing entropy with α using ENI(m+1)−ENI(m) ENI(m) < σ If stopping criteria met go to step 5 else set T = T' and go to step 3 6-convert t' from neutrosophic set to grayscale image…”

mentioning

confidence: 99%

Proposed Framework for Detection of Breast Tumors

Elbaz¹,

Elwahsh²,

El-Henawy³

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge faced by the framework in the segmentation is the speckle noise. It's is a multiplicative and negative issue in breast ultrasound imaging, because of this noise, the image resolution and contrast become reduced, which affects the diagnostic value of this imaging modality. As result, speckle noise reduction is very vital for the segmentation process. The framework uses five models such as Generative Adversarial Denoising Network (DGAN-Net), Denoising U-Shaped Net (D-U-NET), Batch Renormalization U-Net (Br-U-NET), Generative Adversarial Network (GAN), and Nonlocal Neutrosophic of Wiener Filtering (NLNWF) for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio (PSNR) for each level of speckle-noise. The five used methods have been compared with classical filters such as Bilateral, Frost, Kuan, and Lee and they proved their efficiency according to PSNR in different levels of noise. The five diverse models are achieved PSNR results for speckle noise at level (0.

show abstract

Segmentation and denoising of phase contrast MRI image of the aortic lumen via fractal and morphological processing

Cited by 4 publications

References 3 publications

Comparison of CT and MRI in imaging diagnosis of aortic dissection

Comparison of CT and MRI in imaging diagnosis of aortic dissection

Pothole Region Extraction Based On Similarity Evaluation Scale Classification Using Image Processing

Proposed Framework for Detection of Breast Tumors

Contact Info

Product

Resources

About