Nikolaos Giannakeas

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Microarrays are widely used to quantify gene expression levels. Microarray image analysis is one of the tools, which are necessary when dealing with vast amounts of biological data. In this work we propose a new method for the automated analysis of microarray images. The proposed method consists of two stages: gridding and segmentation. Initially, the(More)
Microarray technology provides a tool for the simultaneous analysis of the expression level for an amount of genes. Microarray studies have been shown that image processing techniques can significantly influence microarray data precision. In this paper we propose a supervised method for the segmentation of microarray images based on classification(More)
The selection of a personalized treatment plan for a patient with cancer can be of critical importance for his health or even survival. A Decision Support Platform that can associate the patient clinical situation with the patient DNA Single Nucleotide Polymorphisms (SNPs) can provide the oncologist with a better understanding of the personalized conditions(More)
In this work, an efficient method for spot addressing in images, which are generated by the scanning of hexagonal structured microarrays, is proposed. Initially, the blocks of the image are separated using the projections of the image. Next, all the blocks of the image are processed separately for the detection of each spot. The spot addressing procedure(More)
Microarray technology is a powerful tool for analyzing the expression of a large number of genes in parallel. A typical microarray image consists of a few thousands of spots which determine the level of gene expression in the sample. In this paper we propose a method which automatically addresses each spot area in the image. Initially, a preliminary(More)
In this paper the methodology of designing a genomic-based point-of-care diagnostic system composed of a microfluidic Lab-On-Chip, algorithms for microarray image information extraction and knowledge modeling of clinico-genomic patient data is presented. The data are processed by genome wide association studies for two complex diseases: rheumatoid arthritis(More)
Collagen Proportional Area (CPA) extraction using digital image analysis (DIA) in liver biopsies provides an effective way to estimate the liver disease staging. CPA represents accurately fibrosis expansion in liver tissue. This paper presents an automated clustering-based method for fibrosis detection and CPA computation. Initially, a k-means based(More)
IntroductIon Computer aided medical diagnosis is one of the most important research fields in biomedical engineering. Most of the efforts made focus on diagnosis based on clinical features. The latest breakthroughs of the technology in the biomolecular sciences are a direct cause of the explosive growth of biological data available to the scientific(More)
Microarray technology provides a powerful tool for the quantification of the expression level for a large number of genes simultaneously. Image analysis Is a crucial step for data extraction of microarray gene expression experiments. In this paper we propose a supervised method for the segmentation of microarray Images. The Bayes classifier Is employed for(More)
In this paper an assay for the detection of genes associated with rheumatoid arthritis (RA) and multiple sclerosis, using polymerase chain reaction (PCR) and sequence specific oligonucleotide probes (SSOP) is presented, in order to be further applied in a portable Lab-On-Chip (LOC) device. A substantial part of these reagents were based on the literature(More)