Aristotle Chatziioannou

Automated angiogenesis quantification through advanced image processing techniques

Angiogenesis, the formation of blood vessels in tumors, is an interactive process between tumor, endothelial and stromal cells in order to create a network for oxygen and nutrients supply, necessary for tumor growth. According to this, angiogenic activity is considered a suitable method for both tumor growth or inhibition detection. The angiogenic potential is usually estimated by counting the number of blood vessels in particular sections. One of the most popular assay tissues to study the angiogenesis phenomenon is the developing chick embryo and its chorioallantoic membrane (CAM), which is a highly vascular structure lining the inner surface of the egg shell. The aim of this study was to develop and validate an automated image analysis method that would give an unbiased quantification of the micro-vessel density and growth in angiogenic CAM images. The presented method has been validated by comparing automated results to manual counts over a series of digital chick embryo photos. The results indicate the high accuracy of the tool, which has been thus extensively used for tumor growth detection at different stages of embryonic development

An open data mining framework for the analysis of medical images: Application on Obstructive Nephropathy microscopy images

This paper presents an open image-mining framework that provides access to tools and methods for the characterization of medical images. Several image processing and feature extraction operators have been implemented and exposed through Web Services. Rapid-Miner, an open source data mining system has been utilized for applying classification operators and creating the essential processing workflows. The proposed framework has been applied for the detection of salient objects in Obstructive Nephropathy microscopy images. Initial classification results are quite promising demonstrating the feasibility of automated characterization of kidney biopsy images.

A Collaborative Biomedical Image-Mining Framework: Application on the Image Analysis of Microscopic Kidney Biopsies

The analysis and characterization of biomedical image data is a complex procedure involving several processing phases, such as data acquisition, preprocessing, segmentation, feature extraction, and classification. The proper combination and parameterization of the utilized methods are heavily relying on the given image dataset and experiment type. They may thus necessitate advanced image processing and classification knowledge and skills from the side of the biomedical expert. In this study, an application, exploiting web services and applying ontological modeling, is presented, to enable the intelligent creation of image-mining workflows. The described tool can be directly integrated to the RapidMiner, Taverna or similar workflow management platforms. A case study dealing with the creation of a sample workflow for the analysis of kidney biopsy microscopy images is presented to demonstrate the functionality of the proposed framework.

Delineation and interpretation of gene networks towards their effect in cellular physiology- A reverse engineering approach for the identification of critical molecular players, through the use of ontologies

Exploiting ontologies, provides clues regarding the involvement of certain molecular processes in the cellular phenotypic manifestation. However, identifying individual molecular actors (genes, proteins, etc.) for targeted biological validation in a generic, prioritized, fashion, based in objective measures of their effects in the cellular physiology, remains a challenge. In this work, a new meta-analysis algorithm is proposed for the holistic interpretation of the information captured in -omic experiments, that is showcased in a transcriptomic, dynamic, DNA microarray dataset, which examines the effect of mastic oil treatment in Lewis lung carcinoma cells. Through the use of the Gene Ontology this algorithm relates genes to specific cellular pathways and vice versa in order to further reverse engineer the critical role of specific genes, starting from the results of various statistical enrichment analyses. The algorithm is able to discriminate candidate hub-genes, implying critical biochemical cross-talk. Moreover, performance measures of the algorithm are derived, when evaluated with respect to the differential expression gene list of the dataset.

Building in-silico pathway SBML models from heterogeneous sources

The recent revolutionary developments concerning the high throughput (-omics) measuring techniques in life sciences is expediting the way for the development of in silico models envisioning the systems biology perspective in the description of biological problems. As a result, very large open biological databases provide in silico descriptions in various formats, of biochemical pathways related to various cellular physiological aspects across the evolutionary climax. However, the lack of standardization regarding conceptual biological data representation incurs sheer limitations with respect to the functionality as well as the scientific completeness of the respective models. In this work, a software solution is presented which successfully bridges the gap towards building in-silico metabolic pathway models in Systems Biology Markup Language (SBML) format (standard SBML, CellDesigner SBML) by exploiting various XML based formats (SBML, KGML- KEGG Markup Language-, CellML - Cell Markup Language-, for pathway representation). Our solution provides methods for the biochemically correct transformation, curation and automatic simulation of the pathways, thus accomplishing the setup of fully functional in-silico models.

An open web services - based framework for data mining of biomedical image data

Mining of biomedical image data is a complex procedure that requires several processing phases, such as data acquisition, preprocessing (e.g., image enhancement, color processing), feature extraction and classification. Tools exist that provide each one of these functions individually, however proper integration is required for complex image analysis tasks. This paper presents an open framework based on Web Services that provides access to tools and methods for data mining of biomedical image data. The described tools implemented as Web Services can be directly integrated to the TAVERNA or a similar workflow management platform, allowing their integration in several workflows corresponding to different image processing pipelines. Proper authentication and encryption mechanisms have been utilized in order to guarantee the appropriate security. A case study of classification of skin lesion images is presented to demonstrate the functionality of the proposed framework.

Advanced Block Detection and Quantification of Fibrotic Areas in Microscopy Images of Obstructive Nephropathy

Obstructive nephropathy is not a rare disease and experts need a tool, which will provide them fast and accurate reproducible results for disease assessment. In this work we deal with the analysis of biopsy images for the detection and quantification of obstructive nephropathy. The problem is analyzed on a 3-stage approach. Block based segmentation is applied on the images. Image characterization is achieved through the classification of the informative part of the image utilizing Random Forests classifiers. The second approach deals with characterization of each block separately. Each block was classified with the above classifier and the majority vote of the blocks characterized the whole image. Additionally, a scoring system, based on the characterization of the segmentation blocks, was developed in order to describe and quantify the pathology in an image.

Advanced characterization of microscopic Kidney biopsies utilizing image analysis techniques

Correct annotation and identification of salient regions in Kidney biopsy images can provide an estimation of pathogenesis in obstructive nephropathy. This paper presents a tool for the automatic or manual segmentation of such regions along with methodology for their characterization in terms of the exhibited pathology. The proposed implementation is based on custom code written in Java and the utilization of open source tools (i.e. RapidMiner, ImageJ). The corresponding implementation details along with the initial evaluation of the proposed integrated system are also presented in the paper.

Intelligent planning of biomedical image mining workflows

The analysis and characterization of biomedical image data is a complex procedure involving several processing phases, like data acquisition, preprocessing, segmentation, feature extraction and classification. The proper combination and parameterization of the utilized methods are heavily relying on the given image data set and experiment type and may thus require advanced image processing and classification knowledge from the side of the biomedical expert. In this work, an application, exploiting web services and applying ontological modeling, is presented, to enable the intelligent creation of image mining workflows. The described tool can be directly integrated to the Rapid-Miner or similar workflow management platforms. A case study dealing with the creation of a sample workflow for kidney biopsy microscopy images analysis is presented to demonstrate the functionality of the proposed framework.

Using Grid Infrastructure for the Promotion of Biomedical Knowledge Mining

Transcriptomic technologies (DNA microarrays, Next generation sequencers) represent a major innovation in biomedical research contributing an unprecedented wealth of data regarding genome-wide inspection of an organism. GRISSOM web application is a microarray analysis environment, exploiting Grid technologies. In this work we present how the novel functionalities it incorporates through the use of various web services, gradually transform it to a generic paradigm for versatile biological computing, semantic mining and knowledge discovery.