Effect of filters on segmentation-free geometric verification by X-ray CT

A method has been proposed to verify geometric tolerances by X-ray computed tomography (XCT) without the need for image segmentation The method is based on the direct comparison of a part XCT image to a volumetric representation of its geometric tolerance. In previous works the method was directly applied to raw images. However, filters are commonly applied to XCT images. Usually, they mitigate noise or enhance details. In this work, we study if the segmentation-free verification benefits from the application of filters to XCT images. Standard filters a considered, e.g. Gaussian and non-local means

КРИТЕРИИ ОЦЕНКИ КАЧЕСТВА МЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ, ПОЛУЧЕННЫХ НА МУЛЬТИСПИРАЛЬНОМ КОМПЬЮТЕРНОМ ТОМОГРАФЕ

Оценка качества изображений, особенно медицинских изображений, полученных с помощью мультиспирального компьютерного томографа чрезвычайно важна в области медицинской визуализации. На качество медицинского изображения влияют различные факторы, в том числе характеристики устройства медицинской визуализации и используемый протокол визуализации. Кроме того, наличие шумов, артефактов и других факторов, снижающих качество изображения, может существенно повлиять на общее качество и диагностическую ценность получаемых изображений

КРИТЕРИИ ОЦЕНКИ КАЧЕСТВА МЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ, ПОЛУЧЕННЫХ НА МУЛЬТИСПИРАЛЬНОМ КОМПЬЮТЕРНОМ ТОМОГРАФЕ

Оценка качества изображений, особенно медицинских изображений, полученных с помощью мультиспирального компьютерного томографа чрезвычайно важна в области медицинской визуализации. На качество медицинского изображения влияют различные факторы, в том числе характеристики устройства медицинской визуализации и используемый протокол визуализации

An adaptive image inpainting method based on the modified mumford-shah model and multiscale parameter estimation

Image inpainting is a process of filling missing and damaged parts of image. By using the Mumford-Shah image model, the image inpainting can be formulated as a constrained optimization problem. The Mumford-Shah model is a famous and effective model to solve the image inpainting problem. In this paper, we propose an adaptive image inpainting method based on multiscale parameter estimation for the modified Mumford-Shah model. In the experiments, we will handle the comparison with other similar inpainting methods to prove that the combination of classic model such the modified Mumford-Shah model and the multiscale parameter estimation is an effective method to solve the inpainting problem

BLOOD VESSELS SEGMENTATION METHOD FOR RETINAL FUNDUS IMAGES BASED ON ADAPTIVE PRINCIPAL CURVATURE AND IMAGE DERIVATIVE OPERATORS

Diabetes is a common disease in the modern life. According to WHO’s data, in 2018, there were 8.3% of adult population had diabetes. Many countries over the world have spent a lot of finance, force to treat this disease. One of the most dangerous complications that diabetes can cause is the blood vessel lesion. It can happen on organs, limbs, eyes, etc. In this paper, we propose an adaptive principal curvature and three blood vessels segmentation methods for retinal fundus images based on the adaptive principal curvature and images derivatives: the central difference, the Sobel operator and the Prewitt operator. These methods are useful to assess the lesion level of blood vessels of eyes to let doctors specify the suitable treatment regimen. It also can be extended to apply for the blood vessels segmentation of other organs, other parts of a human body. In experiments, we handle proposed methods and compare their segmentation results based on a dataset – DRIVE. Segmentation quality assessments are computed on the Sorensen-Dice similarity, the Jaccard similarity and the contour matching score with the given ground truth that were segmented manually by a human

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image