Publications by authors named "Petroudi S"

The antibacterial effects of chitosan have been widely studied, but the underlying molecular mechanisms are not fully understood. We investigated the molecular responses of Escherichia coli MG1655 cell, a model gram-negative bacterium, upon exposure to chitosan (Cs), alkylated Cs (AlkCs), and chitosan nanoparticles (CsNPs). Nine target genes involved in relevant signaling pathways (ompF, ompC, ompA, mrcA, mrcB, mgtA, glnA, kdpA, lptA) were selected for analysis.

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Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging, especially in mild cognitive impairment (MCI) subjects. Quantitative structural magnetic resonance imaging acquisition methods in combination with computer-aided diagnosis are currently being used for the assessment of AD. These acquisitions methods include voxel-based morphometry, volumetric measurements in specific regions of interest (ROIs), cortical thickness measurements, shape analysis, and texture analysis.

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Non-invasive ultrasound imaging of carotid plaques can provide information on the characteristics of the arterial wall including the size, morphology and texture of the atherosclerotic plaques. Several studies were carried out that demonstrated the usefulness of these feature sets for differentiating between asymptomatic and symptomatic plaques and their corresponding cerebrovascular risk stratification. The aim of this study was to develop predictive modelling for estimating the time period of a stroke event by determining the risk for short term (less or equal to three years) or long term (more than three years) events.

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Introduction: This study investigates the application of texture analysis methods on brain T2-white matter lesions detected with magnetic resonance imaging (MRI) for the prognosis of future disability in subjects diagnosed with clinical isolated syndrome (CIS) of multiple sclerosis (MS).

Methods: Brain lesions and normal appearing white matter (NAWM) from 38 symptomatic untreated subjects diagnosed with CIS as well as normal white matter (NWM) from 20 healthy volunteers, were manually segmented, by an experienced MS neurologist, on transverse T2-weighted images obtained from serial brain MR imaging scans (0 and 6-12 months). Additional clinical information in the form of the Expanded Disability Status Scale (EDSS), a scale from 0 to 10, which provides a way of quantifying disability in MS and monitoring the changes over time in the level of disability, were also provided.

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The robust border identification of atherosclerotic carotid plaque, the corresponding degree of stenosis of the common carotid artery (CCA), and also the characteristics of the arterial wall, including plaque size, composition, and elasticity, have significant clinical relevance for the assessment of future cardiovascular events. To facilitate the follow-up and analysis of the carotid stenosis in serial clinical investigations, we propose and evaluate an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound videos of the CCA based on video frame normalization, speckle reduction filtering, M-mode state-based identification, parametric active contours, and snake segmentation. Initially, the cardiac cycle in each video is identified and the video M-mode is generated, thus identifying systolic and diastolic states.

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The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the arterial wall including plaque size, composition and elasticity represent important predictors used in the assessment of the risk for future cardiovascular events. This paper proposes and evaluates an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound video of the CCA based on normalization, speckle reduction filtering (with the hybrid median filter) and parametric active contours. The algorithm is initialized in the first video frame of the cardiac cycle with human assistance and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames.

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The segmentation of the intima-media complex (IMC) of the common carotid artery (CCA) wall is important for the evaluation of the intima media thickness (IMT) on B-mode ultrasound (US) images. The IMT is considered an important marker in the evaluation of the risk for the development of atherosclerosis. The fully automated segmentation algorithm presented in this article is based on active contours and active contours without edges and incorporates anatomical information to achieve accurate segmentation.

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Carotid plaques have been associated with ipsilateral neurological symptoms. High-resolution ultrasound can provide information not only on the degree of carotid artery stenosis but also on the characteristics of the arterial wall including the size and consistency of atherosclerotic plaques. The aim of this study is to determine whether the addition of ultrasonic plaque texture features to clinical features in patients with asymptomatic internal carotid artery stenosis (ACS) improves the ability to identify plaques that will produce stroke.

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The thickness of the intima-media complex (IMC) of the common carotid artery (CCA) wall is important in the evaluation of the risk for the development of atherosclerosis. This paper presents a fully automated algorithm for the segmentation of the IMC. The segmentation of the IMC of the CCA wall is important for the evaluation of the intima media thickness (IMT) on B-mode ultrasound images.

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Carotid plaques have been associated with ipsilateral neurological symptoms. High-resolution ultrasound can provide information not only on the degree of carotid artery stenosis but also on the characteristics of the arterial wall including the size and consistency of atherosclerotic plaques. The aim of this study was to determine cerebrovascular risk stratification based on ultrasonic plaque texture features and clinical features in patients with asymptomatic internal carotid artery (ICA) stenosis.

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Breast density has been shown to be one of the most significant risks for developing breast cancer, with women with dense breasts at four to six times higher risk. The Breast Imaging Reporting and Data System (BI-RADS) has a four class classification scheme that describes the different breast densities. However, there is great inter and intra observer variability among clinicians in reporting a mammogram's density class.

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The circumferential resection margin (CRM) has been shown to be a good predictor of the outcome of the total mesorectal resection surgical procedure in colorectal cancer. The CRM is increasingly used as a key parameter in patient management for colorectal cancer. It has been proposed that CRM can be estimated from MRI volumes taken pre-operatively; but to date the estimation of CRM is slice-based, hence two dimensional.

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Apart from chemoradiotherapy, surgery by total mesorectal resection is currently the only curative therapy for colorectal cancer. However, this often has a poor outcome, especially if there are affected lymph nodes too close to the resection boundary. The circumferential resection margin (CRM) is defined as the shortest distance from an affected region to the mesorectal fascia (MF), and should be at least 1 mm.

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Intelligent management of medical data is an important field of research in clinical information and decision support systems. Such systems are finding increasing use in the management of patients known to have, or suspected of having, breast cancer. Different types of breast-tissue patterns convey semantic information which is reported by the radiologist when reading mammograms.

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