Arterial physiology relies on a delicate three-dimensional (3D) organization of cells and extracellular matrix, which is remarkably altered by vascular diseases like abdominal aortic aneurysms (AAA). The ability to explore the micro-histology of the aorta wall is important in the study of vascular pathologies and in the development of vascular constitutive models, i.e., mathematical descriptions of biomechanical properties of the wall. The present study reports and validates a fast image processing sequence capable of quantifying collagen fiber organization from histological stains. Powering and re-normalizing the histogram of the classical fast Fourier transformation (FFT) is a key step in the proposed analysis sequence. This modification introduces a powering parameter w, which was calibrated to best fit the reference data obtained using classical FFT and polarized light microscopy (PLM) of stained histological slices of AAA wall samples. The values of w = 3 and 7 give the best correlation (Pearson's correlation coefficient larger than 0.7, R 2 about 0.7) with the classical FFT approach and PLM measurements. A fast and operator independent method to identify collagen organization in the arterial wall was developed and validated. This overcomes severe limitations of currently applied methods like PLM to identify collagen organization in the arterial wall.
We discuss a remarkable brightening in a polar plume, as inferred from unique coordinated observations of the whitelight corona during the total eclipse of the Sun of 2006 March 29. The polar plume (also known as a polar ray, with distinctions that we discuss) was observed at the positional angle of 9 ; the velocity at which the brightening propagated was about 65 km s À1 , which is close to the values derived by modeling of mass/energy transfer in polar plumes/rays as well as to those acquired from images from the Extreme-ultraviolet Imaging Telescope on the European Space Agency/ NASA Solar and Heliospheric Observatory (SOHO/EIT). Comparing our data with those from the SOHO/LASCO C2 coronagraph, we estimate the lifetime of the polar ray to be less than 24 hr.
Images of the corona have a high dynamic range which is excellent for quantitative photometric analysis. To understand the processes governing the solar corona, it is essential to have information about the absolute brightness as well as the underlying structure. However, due to the steep radial gradient of brightness in the images, and to the fact that structures closer to the solar disk have higher contrast than structures further from the disk, human vision cannot view the intricate structure of the corona in such images. The recently developed normalizing-radial-graded filter (NRGF) is an effective way for revealing the coronal structure. In this work, we present a more adaptive filter inspired by the NRGF, which we call the Fourier normalizing-radial-graded filter (FNRGF). It approximates the local average and the local standard deviation by a finite Fourier series. This method enables the enhancement of finer details, especially in regions of lower contrast. We also show how the influence of additive noise is reduced by a modification to the FNRGF. To illustrate the power of the method, the FNRGF is applied to images of emission from coronal forbidden lines observed during the 2010 July 11 total solar eclipse. It is also successfully applied to space-based observations of the low corona in the extreme ultraviolet and to white light coronagraph observations, thus demonstrating the validity of the FNRGF as a new tool that will help the interpretation of the information embedded in most types of coronal images.
Abstract. In this paper, we present a processing method for digital images from an optical microscope. High-pass type filters are generally used for image focusing. They enhance the high spatial frequencies. These filters are not appropriate if the lack of sharpness is caused by other factors. On the other hand, the (un)sharpness can be taken as an advantage and can be used for studies of the spatial distribution of structures in the observed scene. In many cases, it is possible to construct a threedimensional model of the observed object by analyzing image sharpness. Interesting two-dimensional images and a three-dimensional model can be obtained by applying the theory for multifocal image processing described in this paper. We improve the quality of the results compared to the previous methods using the Fourier transform for the analysis of local sharpness in the images.
Man-made vitreous fibers (MMVFs) are noncrystalline substances made of glass, rock or slag and are widely used as thermal or acoustic insulation materials. There is continued concern about their potential health impacts and thus, their dosimetry and behavior in the environment still require study using filters to collect fiber samples. After deposition or exposure measurements of MMVFs it is often necessary to analyze the filters with deposited fibers. This task is tedious, time-consuming, and requires skill. Therefore, many researchers have tried to simplify or automatize fiber detection and quantification. This article describes features of our in-house software, which automatically detects and counts fibers in images of filter samples. The image analysis is based on the use of a histogram equalization and an adaptive radial convolution filter that enhances fiber contrast and thus, improves the fiber identification. The accuracy of the software analysis was verified by comparison with manual counting using ordinary phase-contrast microscopy method. The correlation between the methods was very high (coefficient of determination was 0.977). However, there were some discrepancies caused by false identifications, which led to implementation of manual corrective functions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.