the ureter has variable trajectory-especially in the parietal segment of the pelvic area. the ureter has an intimate relation with high caliber elements which pump blood into the uterus and the annexes - the lombo-ovarian ligament (at the level of the upper strait basin) and the uterine artery in the broad ligament of the uterus, and thus, it can be ligature-clipped and cut together with them the ureter can also be angulated, with the appearance of a secondary obstruction, if the ligature of ovarian or uterine artery is made very close to the ureter. the ureter is a retro and subperitoneal organ but it also remains adherent to the posterior parietal peritoneum together with which it moves when this one is mobilized and thus it can be mistaken for a fold of the peritoneal and cut. Risk factors determined by the normal anatomy of the ureter: The objective of this project was to develop a fully automated computer vision system that can count tokens used at festivals for buying food and drinks.Risk factors for an iatrogenic ureteral injury Supervisors: Wim Rutten, Michel Van Putten, Chin TangwiriyasakulĮxtraction of the Subthalamic Nucleus from Rat Brain ImagesĮxtraction of certain biological structures from grayscale histological images using a texture descriptor (GLCM) and a classifier (SVM) for segmentation/classification. We extracted the features for different periods (8, 6, 4, and 2 seconds) to see what the influence on all of the above parameters was. Since the eventual aim is to build a system that can be used at home, we examined several electrode configurations in order to find out the minimum number of electrodes needed to control the system. Furthermore, we have employed a spatial filtering method, namely common spatial patterns (CSP), to see if classification outcomes could be improved. For this purpose we have investigated four band power features: broad-band (8 - 30 Hz), α-band (8 - 13 Hz), β-band (13 - 30 Hz), and user-defined band and two classification methods: linear discriminant analysis (LDA) and support vector machines (SVM). Our goal was to find a way to model the two states associated with the sensorimotor rhythm: synchronized (rest) and desynchronized (active). The BCI system should give the patient neurofeedback according to his sensorimotor rhythm. This is done by using electroencephalographic (EEG) signals in a brain computer interface (BCI) setup. The effect of this approach is improved by neurofeedback. One method is the practice of motor imagery. Several methods exist for stroke rehabilitation. #Rmn bucuresti software#The available software should of course be improved and evolved during my thesis.ĪUTOMATIC CLASSIFICATION BETWEEN ACTIVE BRAIN STATE VERSUS REST STATE IN HEALTHY SUBJECTS AND STROKE PATIENTS As the name implies it should take some already available software, like SpectraClassifier (SC) and the INTERPRET Decision Support System (DSS), and transform them into plug-ins for jMRUI. My personal project is supposed to be delivered within 36 months from the start-date. My research falls in the Research Program 2 (RP2) research programme of TRANSACT: “MRS in multi-modal fusion”. This research will be developed in the context of the following EU Initial Training Network (FP7-PEOPLE-2012-ITN): TRANSACT, Transforming Magnetic Resonance Spectroscopy into a Clinical Tool, to which I have been appointed as a Marie Curie Early Stage Researcher (ESR). July 2010 - Bachelor in Applied Electronics, Universitatea Politehnica Bucuresti, Facultatea de electronica, Telecomunicatii si Tehnologia Informatieiĭecision support system and spectral classification tool meta-plug-ins for the jMRUI platform Romanian, English, Italian, French, German, Japaneseĭecember 2012 - MsC in Electrical Engineering - Biomedical Systems, University of Twente Pattern recognition, Signal Processing, Machine Learning, Brain Computer Interface, Magnetic Resonance Spectroscopy, Brain Tumor, Decision-Support Systems
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