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Biomedical subjects

M Zervakis

Publications and source records attributed to M Zervakis.

7 recordsLinked to original sources
PubMed2006

Bioprofiling over grid for eHealthcare.

A trend in modern medicine is towards individualization of healthcare and, potentially, grid computing can play an important role in this by allowing sharing of resources and expertise to improve the quality of care. In this paper, we present a new test bed, the BIOPATTERN Grid, which aims to fulfil this role in the long term. The main objectives in this paper are 1) to report the development of the BIOPATTERN Grid, for biopattern analysis and bioprofiling in support of individualization of healthcare. The BIOPATTERN Grid is designed to facilitate secure and seamless sharing of geographically distributed bioprofile databases and to support the analysis of bioprofiles to combat major diseases such as brain diseases and cancer within a major EU project, BIOPATTERN (www.biopattern.org); 2) to illustrate how the BIOPATTERN Grid could be used for biopattern analysis and bioprofiling for early detection of dementia and for brain injury assessment on an individual basis. We highlight important issues that would arise from the mobility of citizens in the EU, such as those associated with access to medical data, ethical and security; and 3) to describe two grid services which aim to integrate BIOPATTERN Grid with existing grid projects on crawling service and remote data acquisition which is necessary to underpin the use of the test bed for biopattern analysis and bioprofiling.

Computational Biology↗
PubMed2005

Analysis of fibrous proteins from electron microscopy images.

This paper considers an approach for analyzing fibrillar collagen structures from electron microscopy images. It enables the quantitative comparison between collagen structural data (electron-optical data) and chemical data. The particular objectives of the paper are to model the electron microscopy images according to the periodic structure of collagen, provide methods for extracting periodic features directly from the experimental data and propose schemes for comparing these features with the theoretical amino-acid distributions of the examined collagen tissue. Theoretical models in the form of sequence-generated histograms are used as reference for extracting and analyzing the structural unit in images from collagen fibrils. In this respect, collagen provides a valuable model system for studying the chemical basis of ultra-structure and the mechanisms of various treatments on a protein, as well as detecting the alterations in collagen fibril structure produced by a disorder. The algorithms developed in this study can be applied to any fibrous protein, provided that its amino acid sequences and structural properties are known. Several application examples are presented. The algorithmic results are compared with clinical studies as to verify the applicability and potential of the proposed methodology.

Algorithms↗
PubMed2005

An automated approach for analyzing D-periods in collagen fibril images.

This paper considers an approach for analyzing fibrillar collagen structures based on fundamental concepts of pattern recognition. It focuses on the quantitative comparison between collagen structural data (electron-optical data) and chemical data. Theoretical models in the form of sequence-generated histograms are used as reference for extracting and analyzing the structural unit in images from collagen fibrils. In this respect, collagen provides a valuable model system for studying the chemical basis of ultrastructure, as well as detecting the alterations in collagen fibril structure produced by a disorder. Application examples are presented and the results are compared with biochemical studies.

Algorithms↗
PubMed2004

A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification.

In this paper, a hybrid neural network/genetic algorithm technique is presented, aiming at designing a feature extractor that leads to highly separable classes in the feature space. The application upon which the system is built, is the identification of the state of human peripheral vascular tissue (i.e., normal, fibrous and calcified). The system is further tested on the classification of spectra measured from the cell nucleii in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. As advantages of the proposed technique we may encounter the algorithmic nature of the design procedure, the optimized classification results and the fact that the system performance is less dependent on the classifier type to be used.

Journal Article↗
PubMed2003

Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration.

Assessment of the risk for the development of age-related macular degeneration requires reliable detection and quantitative mapping of retinal abnormalities that are considered as precursors of the disease. Typical signs for the latter are the so-called drusen that appear as abnormal white-yellow deposits on the retina. Segmentation of these features using conventional image analysis methods is quite complicated mainly due to the non-uniform illumination and the variability of the pigmentation of the background tissue. This paper presents a novel segmentation algorithm for the automatic detection and mapping of drusen in retina images acquired with the aid of a digital Fundus camera. We employ a modified adaptive histogram equalization, namely the multilevel histogram equalization (MLE) scheme, for enhancing local intensity structures. For the detection of drusen in retina images, we develop a novel segmentation technique, the histogram-based adaptive local thresholding (HALT), which extracts the useful information from an image without being affected by the presence of other structures. We provide experimental results from the application of our technique to real images, where certain abnormalities (drusen) have slightly different characteristics from the background. The performance of the algorithm is established through statistical analysis of the results. This analysis indicates that the proposed drusen detector gives reliable detection accuracy in both position and mass size.

Algorithms↗
PubMed2001

Artificial neural networks for discriminating pathologic from normal peripheral vascular tissue.

The identification of the state of human peripheral vascular tissue by using artificial neural networks is discussed in this paper. Two different laser emission lines (He-Cd, Ar+) are used to excite the chromophores of tissue samples. The fluorescence spectrum obtained, is passed through a nonlinear filter based on a high-order (HO) neural network neural network (NN) [HONN] whose weights are updated by stable learning laws, to perform feature extraction. The values of the feature vector reveal information regarding the tissue state. Then a classical multilayer perceptron is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered. Our method achieves not only the discrimination between normal and pathologic human tissue, but also the successful discrimination between the different types of pathologic tissue (fibrous, calcified). Furthermore, the small time needed to acquire and analyze the fluorescence spectra together with the high rates of success, proves our method very attractive for real-time applications.

Arteriosclerosis↗