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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Treatment effectiveness holds considerable importance in the association between service quality and satisfaction in medical service studies...
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Vol.25 No.2 from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/1P7bHxT via IFTTT
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Publication date: Available online 16 January 2017 Source: International Journal of Pediatric Otorhinolaryngology Author(s): Kaveh Karimn...
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Omalizumab is a treatment for asthma that has a success rate of approximately 40% to 50%.1 Recent mechanistic studies have suggested that ba...
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Understanding Head And Neck Cancers - The Southeast Sun ... The Southeast Sun (NAPSI)—According to the Centers for Disease Control an...
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Abstract Background Henoch–Schönlein purpura is the most common vasculitis in children. Its long-term prognosis depends on renal involve...
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