Motivated by the ALARA (As Low As Reasonably Achievable) principle, this paper proposes to denoise Computed Tomography (CT) images by using a double-filtering approach. First, projection data were filtered using metho...
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Motivated by the ALARA (As Low As Reasonably Achievable) principle, this paper proposes to denoise Computed Tomography (CT) images by using a double-filtering approach. First, projection data were filtered using methods to filter Poisson noise (pre-filtering step). Then the filtered back projection (FBP) algorithm was applied to image reconstruction. After, the reconstructed images were denoised by using suitable methods for filtering Gaussian noise (post-filtering step). Finally, known metrics of image quality evaluation (such as SSIM and PSNR) were used to compare the filtered images with the ones considered ideal images in various combinations of filters. The results lead to the conclusion that a second filtering applied on image domain can improve the CT denoising quality from pre-filtering step. Thus, CT double-filtering strategy achieved a better balance between noise reduction and details preservation.
Many normal and pathological biological processes involve the migration of epithelial cell sheets. This arises from complex emergent behaviour resulting from the interplay between cellular signalling networks and the ...
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Many normal and pathological biological processes involve the migration of epithelial cell sheets. This arises from complex emergent behaviour resulting from the interplay between cellular signalling networks and the forces that physically couple the cells. Here, we demonstrate that collective migration of an epithelium can be interactively guided by applying electric fields that bias the underlying signalling networks. We show that complex, spatiotemporal cues are locally interpreted by the epithelium, resulting in rapid, coordinated responses such as a collective U-turn, divergent migration, and unchecked migration against an obstacle. We observed that the degree of external control depends on the size and shape of the cell population, and on the existence of physical coupling between cells. Together, our results offer design and engineering principles for the rational manipulation of the collective behaviour and material properties of a tissue.
Three-dimensional models have been used to aid medical diagnoses, using images generated by modalities like Magnetic Resonance Imaging. They can provide a more complete vision of objects since their depth is taken int...
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Three-dimensional models have been used to aid medical diagnoses, using images generated by modalities like Magnetic Resonance Imaging. They can provide a more complete vision of objects since their depth is taken into account. Content-based Image Retrieval (CBIR) has also been used to aid the diagnosis. One important step in Three-dimensional CBIR (Model Retrieva) systems is the comparison between two models by using a set of features extracted and stored in a database. In this paper we present a novel method to compare two models, using the Bipartite graphs technique, with the aim to improve the retrieval precision. This technique retrieves 3D medical models of the left ventricle in order to aid the diagnosis of Congestive Heart Failure. Results showed that the novel method improved the precision by 10% when compared to the Similarity Function of Euclidean and Manhattan distance. These results confirmed that bipartite graph techniques can be used to improve the accuracy of Model Retrieval systems.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than ...
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作者:
Kacorri, HernisaHarper, AllenHuenerfauth, Matt
Doctoral Program in Computer Science Graduate Center 365 Fifth Ave New York NY 10016 United States
Computer Science Department CUNY Queens College 65-30 Kissena Blvd Flushing NY 11367 United States
Our lab has conducted experimental evaluations of ASL animations, which can increase accessibility of information for signers with lower literacy in written languages. Participants watch animations and answer carefull...
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A rapid antibiotic susceptibility test (AST) is desperately needed in clinical settings for fast and appropriate antibiotic administration. Traditional ASTs, which rely on cell culture, are not suitable for urgent cas...
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A rapid antibiotic susceptibility test (AST) is desperately needed in clinical settings for fast and appropriate antibiotic administration. Traditional ASTs, which rely on cell culture, are not suitable for urgent cases of bacterial infection and antibiotic resistance owing to their relatively long test times. Here, we describe a novel AST called single-cell morphological analysis (SCMA) that can determine antimicrobial susceptibility by automatically analyzing and categorizing morphological changes in single bacterial cells under various antimicrobial conditions. The SCMA was tested with four Clinical and Laboratory Standards Institute standard bacterial strains and 189 clinical samples from hospitals. The results were compared with the gold standard broth microdilution test. The SCMA results were obtained in less than 4 hours with 91.5% categorical agreement and 6.51% minor, 2.56% major, and 1.49% very major discrepancies. Thus, SCMA provides rapid and accurate antimicrobial susceptibility data.
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