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Search Results: 1 - 10 of 335 matches for " Sergiu Nedevschi "
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Influence of Expert-Dependent Variability over the Performance of Noninvasive Fibrosis Assessment in Patients with Chronic Hepatitis C by Means of Texture Analysis
Cristian Vicas,Monica Lupsor,Mihai Socaciu,Sergiu Nedevschi,Radu Badea
Computational and Mathematical Methods in Medicine , 2012, DOI: 10.1155/2012/346713
Abstract: Texture analysis is viewed as a method to enhance the diagnosis power of classical B-mode ultrasound image. The present paper aims to evaluate and eliminate the dependence between the human expert and the performance of such a texture analysis system in predicting the cirrhosis in chronic hepatitis C patients. 125 consecutive chronic hepatitis C patients were included in this study. Ultrasound images were acquired from each patient and four human experts established regions of interest. Textural analysis tool was evaluated. The performance of this approach depends highly on the human expert that establishes the regions of interest (<0.05). The novel algorithm that automatically establishes regions of interest can be compared with a trained radiologist. In classical form met in the literature, the noninvasive diagnosis through texture analysis has limited utility in clinical practice. The automatic ROI establishment tool is very useful in eliminating the expert-dependent variability.
Iterative Methods for Obtaining Energy-Minimizing Parametric Snakes with Applications to Medical Imaging
Alexandru Ioan Mitrea,Radu Badea,Delia Mitrea,Sergiu Nedevschi,Paulina Mitrea,Dumitru Mircea Ivan,Octavian Mircia Gurz u
Computational and Mathematical Methods in Medicine , 2012, DOI: 10.1155/2012/918510
Abstract: After a brief survey on the parametric deformable models, we develop an iterative method based on the finite difference schemes in order to obtain energy-minimizing snakes. We estimate the approximation error, the residue, and the truncature error related to the corresponding algorithm, then we discuss its convergence, consistency, and stability. Some aspects regarding the prosthetic sugical methods that implement the above numerical methods are also pointed out.
Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images
Delia Mitrea,Paulina Mitrea,Sergiu Nedevschi,Radu Badea,Monica Lupsor,Mihai Socaciu,Adela Golea,Claudia Hagiu,Lidia Ciobanu
Computational and Mathematical Methods in Medicine , 2012, DOI: 10.1155/2012/348135
Abstract: The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays. Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images. In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors. We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order. As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue.
Semi-Supervised Segmentation of Ultrasound Images Based on Patch Representation and Continuous Min Cut
Anca Ciurte, Xavier Bresson, Olivier Cuisenaire, Nawal Houhou, Sergiu Nedevschi, Jean-Philippe Thiran, Meritxell Bach Cuadra
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0100972
Abstract: Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.
Sergiu Coseri
Molbank , 2006, DOI: 10.3390/m459
Abstract: No abstract available
Sergiu Coseri
Molbank , 2006, DOI: 10.3390/m460
Abstract: No abstract available
Sergiu Coseri
Molbank , 2006, DOI: 10.3390/m461
Abstract: No abstract available
The Demographic Situation in the Northern Economic Region of the Republic of Moldova
Romanian Review of Regional Studies , 2008,
Abstract: The demographic situation within the Northern Economic Region is worse than the national average. This fact is determined by the decrease of the birth rate values, the increase of the mortality values and the negative natural and migration growth. They also lead to a change of the rate between age groups, in favour of the elderly. In order to diminish such a situation, urgent measures should be taken (policies for economic increase and income increase) at local, department and regional level, to stop the deep demographic crisis of the region.
Sergiu Gherghina
Romanian Journal of European Affairs (RJEA) , 2010,
Abstract: This article investigates the relationship between political knowledge and trust in the EU among the Romanian citizens between 2002 and 2009. It uses individual level data from the Candidate Countries (2001-2004) and Standard Eurobarometers (2005-2009) to check whether there is a direct linkage between the decreasing level of trust and the increasing level of knowledge in the most Euro-optimist new member state. The statistical analysis reveals that the more citizens know about the EU, the more they trust it. Such a result gains supplementary relevance in the context of decreasing support. Two other general results are relevant for the Romanian case. First, although the level of political knowledge increases as the accession gets closer, it continues to be quite low. Second, the Romanian citizens constantly overestimate their knowledge about the EU.
On a Nonlinear Nonlocal Cauchy Problem
Sergiu Aizicovici
Lecture Notes in Engineering and Computer Science , 2009,
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