14,552 research outputs found
Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR
<p>Background: T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.</p>
<p>Methods: We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.</p>
<p>Results: The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.</p>
Fast determination of coarse grained cell anisotropy and size in epithelial tissue images using Fourier transform
Mechanical strain and stress play a major role in biological processes such
as wound healing or morphogenesis. To assess this role quantitatively, fixed or
live images of tissues are acquired at a cellular precision in large fields of
views. To exploit these data, large numbers of cells have to be analyzed to
extract cell shape anisotropy and cell size. Most frequently, this is performed
through detailed individual cell contour determination, using so-called
segmentation computer programs, complemented if necessary by manual detection
and error corrections. However, a coarse grained and faster technique can be
recommended in at least three situations. First, when detailed information on
individual cell contours is not required, for instance in studies which require
only coarse-grained average information on cell anisotropy. Second, as an
exploratory step to determine whether full segmentation can be potentially
useful. Third, when segmentation is too difficult, for instance due to poor
image quality or too large a cell number. We developed a user-friendly, Fourier
transform-based image analysis pipeline. It is fast (typically cells per
minute with a current laptop computer) and suitable for time, space or ensemble
averages. We validate it on one set of artificial images and on two sets of
fully segmented images, one from a Drosophila pupa and the other from a chicken
embryo; the pipeline results are robust. Perspectives include \textit{in vitro}
tissues, non-biological cellular patterns such as foams, and stacks.Comment: 13 pages; 9 figure
Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy
Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes
Automated detection of extended sources in radio maps: progress from the SCORPIO survey
Automated source extraction and parameterization represents a crucial
challenge for the next-generation radio interferometer surveys, such as those
performed with the Square Kilometre Array (SKA) and its precursors. In this
paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source
Automated Recognition), to detect and parametrize extended sources in radio
interferometric maps. It is based on a pre-filtering stage, allowing image
denoising, compact source suppression and enhancement of diffuse emission,
followed by an adaptive superpixel clustering stage for final source
segmentation. A parameterization stage provides source flux information and a
wide range of morphology estimators for post-processing analysis. We developed
CAESAR in a modular software library, including also different methods for
local background estimation and image filtering, along with alternative
algorithms for both compact and diffuse source extraction. The method was
applied to real radio continuum data collected at the Australian Telescope
Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU
survey. The source reconstruction capabilities were studied over different test
fields in the presence of compact sources, imaging artefacts and diffuse
emission from the Galactic plane and compared with existing algorithms. When
compared to a human-driven analysis, the designed algorithm was found capable
of detecting known target sources and regions of diffuse emission,
outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure
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