Spatially aggregated disease-count data relating to a set of nonoverlap-ping areal units are often used to make inference on population-level disease risk. This includes the identification of risk boundaries, which ar...
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Spatially aggregated disease-count data relating to a set of nonoverlap-ping areal units are often used to make inference on population-level disease risk. This includes the identification of risk boundaries, which are locations where there is a sizeable change in risk between geographically neighbouring areal units. Existing studies provide spatially discrete inference on the areal unit footprint, which forces the boundaries to coincide with the entire geo-graphical border between neighbouring units. This paper is the first to relax these assumptions by estimating disease risk and the locations of risk bound-aries on a grid of square pixels covering the study region that can be made arbitrarily small to approximate a spatially continuous surface. We propose a two-stage approach that first fits a Bayesian spatiotemporal realignment model to estimate disease risk at the grid level and then identifies bound-aries in this surface using edgedetectionalgorithms from computer vision. This novel methodological fusion is motivated by a new study of respiratory hospitalisation risk in Glasgow, Scotland, between 2008 and 2017, and we identify numerous risk boundaries across the city.
Comparison of Computerized Tomography(CT)images acquired during planning/treatment procedures is still a challenge in Image Guided Radiotherapy(IGRT).These not only verify patient positioning during treatment procedur...
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Comparison of Computerized Tomography(CT)images acquired during planning/treatment procedures is still a challenge in Image Guided Radiotherapy(IGRT).These not only verify patient positioning during treatment procedure,but also insure radiation dose evaluation supposed to be delivered to target volume.
Speckle in ultrasonic image systems adversely impacts the contrast and resolution in the image. This poses serious problems in the interpretation of B mode images of internal organs such as breast, liver, kidney and s...
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Speckle in ultrasonic image systems adversely impacts the contrast and resolution in the image. This poses serious problems in the interpretation of B mode images of internal organs such as breast, liver, kidney and so on. In the absence of sufficient contrast, classifying the regions of interest into benign and malignant masses becomes error prone. Since some of the masses are uniquely identified in terms of the boundaries, poor contrast and resolution will result in difficulties with their identification. A new class of spatial filters based on cylindrical Bessel functions of the first kind is proposed for speckle reduction. These filters with complex impulse responses were explored for enhancing the contrast of speckled images. Hypothesising that the phase of the filtered image carries boundary information, the phase characteristics of four speckled images are also studied for detecting boundaries. Results indicate that these filters do improve the contrast and enhance the boundaries. It is shown that the phase map clearly indicates the existence of boundaries. A simple thresholding applied to the phase highlights the boundaries. The results show the strength of the Bessel spatial filters in improving contrast and highlighting boundaries without resorting to any additional edge-detection algorithms.
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