We used for comparative analyses BlastP (available at: https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins) and the whole virus proteome (available at: https://www.ncbi.nlm.nih.gov/nuccore/MN908947).
Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.
Abstract-A system generating 1.8 GHz electromagnetic fields for bio-medical and behavioral study on laboratory animals was designed and implemented. The system is based on a reverberation chamber. An input power up to 5 W can be sent to an indoor transmitting antenna and an electric field strength (E) more than 90 V/m can be reached inside the chamber. The system was characterized at different input powers measuring E in different points by means of a miniature sensor. Then, boxes with 300 cc of physiological liquid inside were realized as simple phantoms simulating laboratory animals (rats)Corresponding author: P. F. Biagi (biagi@fisica.uniba.it). and E inside the liquid was measured, performing several simulations by moving the phantoms (1, 2) in the chamber and/or putting them still in different positions. On the basis of these measurements, the SAR (Specific Absorption Rate) and the Pe (power efficiency = SAR/input power) were determined at different powers. The actual system is characterized by a low power efficiency with respect to the "in vivo" exposition systems based on transversal electromagnetic (TEM) cells. Its advantage is to have inside the chamber a habitat similar to the usual one for the laboratory animals.
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