In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric meth...
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Background Hyperthyroidism is associated with increased thrombotic risk. As contact system activation through formation of neutrophil extrac...
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Related Articles Developmental control of macrophage function. Curr Opin Immunol. 2017 Dec 13;50:64-74 Authors: Bonnardel J, Guillia...
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Summary The preventive effect of coffee on cancer at different sites has been reported, although the effect on all-sites cancer incidence ...
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from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/2tcPIjn via IFTTT
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Abstract: Epidermolytic ichthyosis (EI) is a rare disorder of cornification caused by mutations in KRT1 and KRT10, encoding two suprabasal e...
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