Geothermal exploration of kos island, Greece: Magnetotelluric and microseismicity studies

This paper reports the results of magnetotelluric (MT) and microseismicity studies, conducted as part of a multi-disciplinary project to explore the geothermal potential of the island of Kos, Greece. The MT survey, comprising 18 soundings, was carried out in the bandwidth 128 Hz–40 s, in order to determine the deep conductivity structure in the geothermally prospective western part of the island. Rigorous dimensionality analysis has indicated that the geoelectric structure could adequately be approximated with 1-D interpretation tools. Two significant and seemingly communicating conductive zones of potential geothermal interest were found within the first 2 km. The first is extensive and shallow, detected at depths of 400–600 m; the second is deeper (1000–1300 m), but of considerably smaller lateral dimensions. A very deep relative conductor (<25 Ωm) was also detected at depths of 7–10 km, which is thought to comprise part of an old magma chamber with brine-saturated rocks. The microseismicity studies revealed the partial or total attenuation of shear waves in many microearthquake records. The analysis of these observations determined the vertical and lateral extent of that attenuation zone, the greatest part of which is located underneath the marine area between western Kos and Nissyros island to the south, extending approximately from near the surface to about 1.5 km depth. The nature of this zone is discussed in terms of fluid concentration due to the geothermal system of the area.     

Improving the performance of convolutional neural network for skin image classification using the response of image analysis filters

Neural Computing and Applications - In this work, we focus in the analysis of dermoscopy images using convolutional neural networks (CNNs). More specifically, we investigate the value of augmenting...     

Image registration based on lifting process: an application to digital subtraction radiography.

In this paper, a digital subtraction radiology scheme is presented based on a new method for the automatic registration of dental radiographs acquired with or without rigorous a priori standardization. The scheme is comprised of an automatic registration method and a subtraction process. The proposed registration method can be considered as an object-based registration method without imposing the prerequisite of image segmentation in order to detect the boundary of the objects of interest or the automatic detection of matching landmarks. This is achieved by augmenting the dimensionality of the problem from two-dimensional gray-level matching to three-dimensional surface matching using the process of lifting in combination with a surface-matching technique. The pseudo three-dimensional affine transformation that matches the lifted images incorporates advantageous characteristics including spatial alignment of the surfaces, anisotropic correction of brightness/contrast differences, and stable convergence of the similarity function to its optimal value. The performance of the proposed automatic registration method is assessed against a manual method based on the projective transformation. The qualitative and quantitative assessments of the experiments have shown advantageous performance of the proposed automatic registration method against the manual one. Finally, the proposed registration method has been further improved in terms of execution time by the implementation of a surface decimation process.     

The Right of States to Use Force in Cyberspace: Defining the Rules of Engagement

This article examines the circumstances under which states may resort to the use of force in cyberspace. It begins by describing briefly the evolution of the Internet and the subsequent information revolution, as well as the advent of information warfare techniques. After attempting to present some definition of what is currently known as 'information warfare', along with some real-life examples of its application, the focus will be on examining existing international legal norms upon which the right of states to resort to forcible acts in cyberspace is based.     

Multimodal registration of retinal images using self organizing maps

In this paper, an automatic method for registering multimodal retinal images is presented. The method consists of three steps: the vessel centerline detection and extraction of bifurcation points only in the reference image, the automatic correspondence of bifurcation points in the two images using a novel implementation of the self organizing maps and the extraction of the parameters of the affine transform using the previously obtained correspondences. The proposed registration algorithm was tested on 24 multimodal retinal pairs and the obtained results show an advantageous performance in terms of accuracy with respect to the manual registration.     

Automatic retinal image registration scheme using globaloptimization techniques

Retinal image registration is commonly required in order to combine the complementary information in different retinal modalities. In this paper, a new automatic scheme to register retinal images is presented and is currently tested in a clinical environment. The scheme considers the suitability and efficiency of different image transformation models and function optimization techniques, following an initial preprocessing stage. Three different transformation models-affine, bilinear and projective-as well as three optimization techniques-downhill simplex method, simulated annealing and genetic algorithms-are investigated and compared in terms of accuracy and efficiency. The registration of 26 pairs of fluoroscein angiography and indocyanine green chorioangiography images with the corresponding red-free retinal images, showed the superiority of combining genetic algorithms with the affine and bilinear transformation models. A comparative study of the proposed automatic registration scheme against the manual method, commonly used in clinical practice, is finally presented showing the advantage of the proposed automatic scheme in terms of accuracy and consistency     

Genetic algorithm implementation of stack filter design for imagerestoration

Stack filters are a class of nonlinear spatial operators used for noise suppression. Their design is formulated as an optimisation problem and genetic algorithms (GAs) are used to perform the configuration. Applying the mean absolute error (MAE) as the basis of an objective function, the stack filter is used to restore magnetic resonance (MR) images corrupted with uncorrelated additive noise from 10%, and 50%. The filter is trained on corresponding patches of the original and noisy image and then applied to the whole image. The outcomes are compared with the median filter and return a smaller MAE for all noise levels. The dependency of MAE on the training window size and the GA early termination is examined, showing that a reduction of 75% in computational complexity can be achieved by a 10% relaxation in the MAE. The design is then extended from 9-point to 13-point filters and by training on Poisson noise, the filter is applied to nuclear medicine bone scans where no absolute truth exists. Surface topology, image profiles and the measurement of relative contrast show its value in reducing noise whilst preserving contrast. Because of its computational complexity the process has been implemented as a distributed GA using the parallel virtual machine (PVM) software     

Computer-Aided Diagnosis of Thyroid Malignancy Using an Artificial Immune System Classification Algorithm

The diagnosis of thyroid malignancy by fine needle aspiration (FNA) examination has been proven to show wide variations of sensitivity and specificity. This paper proposes the utilization of a computer-aided diagnosis system based on a supervised classification algorithm from the artificial immune systems to assist the task of thyroid malignancy diagnosis. The core of the proposed algorithm is the so-called BoxCells, which are defined as parallelepipeds in the feature space. Properly defined operators act on the BoxCells in order to convert them into individual, elementary classifiers. The proposed algorithm is applied on FNA data from 2016 subjects with verified diagnosis and has exhibited average specificity higher than 99%, 90% sensitivity, and 98.5% accuracy. Furthermore, 24% of the cases that are characterized as ldquosuspiciousrdquo by FNA and are histologically proven nonmalignancies have been classified correctly.     

MITIS: a WWW-based medical system for managing and processing gynecological-obstetrical-radiological data

In this paper a World Wide Web (WWW)-based medical system, called MITIS, is designed and developed for the management and processing of obstetrical, gynecological and radiological medical data. The system records all the necessary medical information in terms of patient data, examinations, and operations and provides the user-expert with advanced image processing tools for the manipulation, processing and storage of ultrasound and mammographic images. The system can be installed in a hospital's Local Area Network (LAN) where it can access picture archival and communication systems (PACS) servers (if available), or any other server within the radiology department, for image archiving and retrieval, based on the digital imaging and communication in medicine (DICOM) 3.0 protocol, over TCP/IP and also it is accessible to external physicians via the hospital's Internet connection. MITIS is composed as a set of independent WWW modules (ISAPI server extension dlls) and a Win32 application (COM+ server) for mammography image processing and evaluation.