M. Zervakis

Stokes parameter imaging of multi-index of refraction biological phantoms utilizing optically active molecular contrast agents

The purpose of this study is to assess the potential of novel molecular polarimetric imaging techniques utilizing multi-index of refraction targets, i.e. composite targets made from optically different media, immersed into biological fluids doped with optically active molecules and enzymes. The outcome of this study indicates that the application of Stokes parameter detection principles with concominant administration of fluids containing suitable optically active molecular contrast agents and high index of refraction molecules could enhance the detection and imaging process of internal structures by providing enhanced penetration depth, high contrast and high depolarized scatter rejection.

Polarimetric phenomenology of photons with lung cancer tissue

The objective of this study is to explore the polarimetric phenomenology of light interaction with healthy and early-stage lung cancer tissue samples by applying efficient polarimetric backscattering detection techniques combined with polarimetric exploratory data analysis. Preliminary results indicate that enhanced discrimination signatures can be obtained for certain types of early-stage lung cancers based on their depolarization, backscattered intensity and retardance characteristics.

Infrared photon discrimination of lung cancer cells

The objective of this study is to explore the polarimetric phenomenology of near infrared light interaction with healthy and lung cancer monolayer cells by using efficient polarimetric transmission detection techniques. Preliminary results indicate that enhanced discrimination between normal and different types of lung cancer cell stages can be achieved based on their transmitted intensities and depolarization properties of the cells. Specifically, the sizes of the nuclei of the cancer cells and the nucleus-to-cytoplasmic ratios appear to have potential impact on the detected polarimetric signatures leading to enhanced discrimination of lung cancer cells.

Near infrared light interaction with lung cancer cells

The objective of this study is to explore the phenomenology of near infrared (NIR) light interaction with healthy and early-lung cancer by combining efficient polarimetric backscattering detection techniques with Polarimetric Exploratory Data Analysis (pEDA). Preliminary results indicate that enhanced discrimination signatures can be obtained for certain types of lung cancers.

Validation of MRS metabolic markers in the classification of brain gliomas and their correlation to energy metabolism

The aim of this study is to validate the significance of recently identified MRS (Magnetic Resonance Spectroscopy) ratio-type metabolic markers used in brain gliomas classification, through the energy metabolism profile of these complex tumors. It is an attempt to integrate the metabolic knowledge extracted from MRS analysis of patient gliomas provided, with proteomic knowledge derived from metabolic enzymes that participate in the energy production process, called glycolysis.

The Role of the Glycolysis-Related Genes in Brain Gliomas Treatment Design

The aim of this study is to reveal the significance of glycolysis-related genes in the design of new brain gliomas treatment protocols. Towards this direction a feature selection and classification method, embedding the Relief-F filter criterion under a Support Vector Machines (SVM) classifier, has been introduced focusing on the identification of significant genetic alterations related to glycolysis. In particular a genomic (Microarray Expression) dataset, consisting of 14 glioma patients has been used for the statistical analysis. The results have demonstrated that a specific group of gene markers (HK, PGI, PFK, ALDO, GAPDH, PGK, PGM, ENO, PK, LDH, PDH and MDH) directly related to cell glycolysis, have a great impact on the discrimination of different grades of gliomas malignancy (AUROC of 0.98), thus leading to the conclusion that these markers could establish the foundations for new therapeutic approaches.

The Impact of Microarray Technology in Brain Cancer

Abstract The analysis of global gene expression patterns (expression profiling) produced from the microarray technology (cDNAs and tissue microarrays), has made important contributions to our understanding of the regulation of biological systems and gene function. Recently, it is becoming increasingly significant for the diagnosis, prognosis and treatment of brain cancer. Conventional methods used for brain tumour diagnosis utilize modalities (CT, MRI, PET, EEG, Biopsy and Lumbar Puncture) that provide medical information. The genomic analysis used to supplement such medical information is expected to provide appropriate tools for early diagnosis and effective therapy of cancer. Microarray-based clustering and classification methods have been used to reclassify the brain tumours already known by the World Health Organization (WHO) and/or discover new sub-types. Unsupervised and supervised classification methods that have been tested on different brain tumour types using gene expression data as input, have shown promising efficiency. This fact offers great potential to the clinicians that are now able to develop new methods of treatment based on gene therapy instead of applying the traditional ones (surgery, radiation, chemotherapy etc.). This chapter attempts to reveal the important role of genomics in brain cancer. Several genomic-based methods for brain cancer analysis are reviewed and compared to traditional ones with an emphasis to DNA microarray technology that was recently introduced. Finally, feature genomic-based developments that will assist the diagnosis, prognosis and treatment of brain cancer are presented.

Embedding filtering criteria into a wrapper marker selection method for brain tumor classification: an application on metabolic peak area ratios

The purpose of this study is to identify reliable sets of metabolic markers that provide accurate classification of complex brain tumors and facilitate the process of clinical diagnosis. Several ratios of metabolites are tested alone or in combination with imaging markers. A wrapper feature selection and classification methodology is studied, employing Fishers criterion for ranking the markers. The set of extracted markers that express statistical significance is further studied in terms of biological behavior with respect to the brain tumor type and grade. The outcome of this study indicates that the proposed method by exploiting the intrinsic properties of data can actually reveal reliable and biologically relevant sets of metabolic markers, which form an important adjunct toward a more accurate type and grade discrimination of complex brain tumors

ARMA modeling for the diagnosis of controlled epileptic activity in young children

Parametric models are widely used for EEG data analysis. In this experimental study an autoregressive moving average (ARMA) model was used to extract spectral features within defined frequency bands which were then used to discriminate a group of children with controlled mild epilepsy from an age- and sex-matched control group. This study differs from other published works in that it shows that this technique can be used as a biomarker to distinguish the epileptic subjects specifically when the EEG recordings of these subjects are clinically diagnosed as normal. Using the spectral features and a linear discriminant classifier a global classification score of up to 85% was achieved on our clinical data. Furthermore the results showed that epileptic children have significantly higher spectral power in frequency bands up to 45 Hz, with the largest difference occurring within the alpha band.

Imaging Systems and Techniques 2011

This special feature on Imaging Systems and Techniques comprises 11 papers, covering essential facets in imaging systems and techniques both in theory and applications, from research groups in three different continents. It mainly contains peer-reviewed articles from the IEEE International Conference on Imaging Systems and Techniques (IST 2011), held in Batu Ferringi, Penang, Malaysia, as well a number of articles relevant to the scope of this issue. Breakthrough discoveries in imaging are promising for an array of disciplines and applications such as the design of highly efficient imaging sensors with enhanced figures of merit, for space-based surveillance and situational awareness missions, missile defense, homeland security, medicine, biology, bioengineering, advanced diagnostic and analytical devices and instrumentation, including nanoinstrumentation. Imaging technologies are expected to play an important role in the protection of space assets and infrastructure such as space satellites, therefore enhancing the space situational awareness (SSA) and mission protection. Innovative and efficient detection and imaging trends for space-based surveillance and situational awareness missions will be emerge as a result of the discovery and development of quantum well structures, quantum dot lasers, nanomaterials, metamaterials and photonic nanocrystals. Imaging technologies relying on optoelectronic detectors and photonic components can be substantially improved by optoeletronic nanostructures, leading to controllable transmission and reflection optical filters and devices, with high agility, tunability, scalability and reconfigurability. The tremendous evolution of nanotechnology promises to provide to molecular imaging an arsenal of novel targeted contrast agents. The desired properties of such targeted contrast agents are: long bioavailability, selective binding to targets of interest and low toxicity profile, while offering high contrast and high background rejection ratio. Advanced metabolic and functional imaging techniques, operating on multiple physical principles, using high resolution, high selectivity nanoimaging techniques, making use of quantum dots, nanoshells, biomarkers, contrast agents, gold nanostructures and PEBBLE (probes encapsulated by biologically localized embedding) nanosensors, will play an important role in the diagnosis and treatment of autoimmune diseases and cancer, as well as providing efficient drug delivery. By integrating multifunctional nanovectors and nanoconjugates with imaging, nanophotonics could give rise to reconfigurable and scalable photonic devices and imaging systems for early diagnosis, accurate staging, better facilitation of localized surgical interventions, treatment of cancer, as well as minimally invasive monitoring of therapeutic response. As a result, this could dramatically improve the current poor survival rate of patients with a variety of tumors by providing imaging solutions for disease treatment with increased sensitivity and specificity. Multifunctional nanoassemblies would enhance the clinical imaging significantly by allowing the incorporation of imaging components, such as near-infrared (NIR) dyes, single photon emission computed tomography (SPECT) agents and positron emission tomography (PT) agents, onto surface mesoporous silicon particles (S1MPs). Efficient digital imaging systems and techniques, and tomographic devices operating on electrical impedance tomography, electromagnetic scattering, computed tomography, SPECT and PT detection principles, are anticipated to have a significant impact on a wide spectrum of technological areas, such as medical imaging, the pharmaceutical industry, analytical instrumentation, aerospace, exploration of resources, remote sensing, lidars and ladars, surveillance, national defense, corrosion imaging and monitoring, sub-terrestrial and marine imaging. Pattern recognition and image processing algorithms can significantly contribute to enhanced detection and imaging, including object classification, clustering, feature selection, texture analysis, segmentation, image compression, and color representation under complex imaging scenarios, with applications in medical imaging, remote sensing, aerospace, radars, defense and homeland security. As a result, the promotion of multidisciplinary research programs and new training programs in the multifaceted area of imaging would facilitate the development of new imaging technologies, nanomaterials, molecular probes and targeted contrast agents. We feel confident that the exciting new contributions of this special feature on Imaging Systems and Techniques will appeal to the technical community. I would like to thank all the authors as well as all anonymous reviewers and the MST Editorial Board and Publisher for their tremendous efforts and invaluable support in enhancing the quality of this significant endeavor.