The assessment of angiogenesis and fibroblastic stromagenesis in hyperplastic and pre-invasive breast lesions

<p>Abstract</p> <p>Background</p> <p>To investigate the changes of the neoplastic microenvironment during the different morphological alterations of hyperplastic and pre-invasive breast lesions.</p> <p>Methods</p> <p>78 in situ ductal carcinomas of all degrees of differentiation, 22 atypical ductal hyperplasias, 25 in situ lobular carcinomas, 18 atypical lobular hyperplasias, 32 ductal epithelial hyperplasias of usual type and 8 flat atypias were immunohistochemically investigated for the expression of vascular endothelial growth factor (VEGF), smooth muscle actin (SMA) and CD34, while microvessel density (MVD) was counted using the anti-CD31 antibody.</p> <p>Results</p> <p>VEGF expression was strongly correlated with MVD in all hyperplastic and pre-invasive breast lesions (p < 0.05). Stromagenesis, as characterized by an increase in SMA and a decrease in CD34 positive myofibroblasts was observed mostly around ducts harboring high grade in situ carcinoma and to a lesser extent around moderately differentiated DCIS. In these two groups of in situ carcinomas, a positive correlation between MVD and SMA (p < 0.05) was observed. On the contrary, CD34 was found to be inversely related to MVD (p < 0.05). No statistically significant changes of the stromal fibroblasts were observed in low grade DCIS neither in any of the other lesions under investigation as compared to normal mammary intra- and interlobular stroma.</p> <p>Conclusion</p> <p>Angiogenesis is observed before any significant fibroblastic stromagenesis in pre-invasive breast lesions. A composite phenotype characterized by VEGF positive epithelial cells and SMA positive/CD34 negative stromal cells, is identified mostly in intermediate and high grade DCIS. These findings might imply for new therapeutic strategies using both anti-angiogenic factors and factors selectively targeting tumor stroma in order to prevent the progression of DCIS to invasive carcinoma.</p

SOUTHERN AEGEAN. MARINE GEOPHYSICAL SURVEY AND STOCHASTIC - DETERMINISTIC APPROACH OF ITS TECTONIC-GEODYNAMIC-GEOTHERMAL STRUCTURE

IN THIS DISSERTATION THE GRAVITY AND MAGNETIC FIELD AS WELL AS THE BATHYMETRY OF THE SOUTHERN AEGEAN SEA WERE RESURVEIED PRECISELY AND PENSELY. CONSEQUENTLY, A COMBINED STOCHASTIC AND DETERMINISTIC APPROACH, WAS APPLIED DURING THE ANALYSIS OF THE DATA, IN ORDER TO REVEAL THE PREDOMINANT GEOMETRY OF THE TECTONIC DEFORMATION OF THE AREA. THE RESULTS SHOWED, THAT A PARTICULAR FRACTURE MECHANISM,COMPRISED BY A CONJUGATION OF SHEAR STRESS ZONES, PLAYS THE PREDOMINANT ROLE. THE COMBINED ACTION, OF THE CONJUGATION, LEADS TO THE OPENING, OF CHARACTERISTIC WIDELY DISTRIBUTED PULL APART BASINS, AT THE AREA.ΣΤΗΝ ΠΑΡΟΥΣΑ ΔΙΔΑΚΤΟΡΙΚΗ ΔΙΑΤΡΙΒΗ ΕΓΙΝΕ ΠΛΗΡΗΣ ΚΑΙ ΛΕΠΤΟΜΕΡΙΑΚΗ ΧΑΡΤΟΓΡΑΦΗΣΗ ΤΟΥ ΠΕΔΙΟΥ ΒΑΡΥΤΗΤΑΣ ΤΟΥ ΜΑΓΝΗΤΙΚΟΥ ΠΕΔΙΟΥ ΚΑΙ ΤΗΣ ΜΟΡΦΟΛΟΓΙΑΣ ΤΟΥ ΠΥΘΜΕΝΑ ΤΟΥ ΝΟΤΙΟΥ ΑΙΓΑΙΟΥ. ΑΚΟΛΟΥΘΩΣ ΕΦΑΡΜΟΣΘΗΚΑΝ ΣΤΟΧΑΣΤΙΚΕΣ ΚΑΙ ΝΤΕΤΕΡΜΙΝΙΣΤΙΚΕΣ ΜΕΘΟΔΟΙ ΑΝΑΛΥΣΗΣ ΤΩΝ ΔΕΔΟΜΕΝΩΝ ΕΤΣΙ ΩΣΤΕ ΝΑ ΑΠΟΚΑΛΥΦΘΕΙ Η ΓΕΩΜΕΤΡΙΚΗ ΕΙΚΟΝΑ ΠΟΥ ΠΑΡΟΥΣΙΑΖΕΙ Η ΤΕΚΤΟΝΙΚΗ ΠΑΡΑΜΟΡΦΩΣΗ ΤΗΣ ΠΕΡΙΟΧΗΣ. ΤΑ ΑΠΟΤΕΛΕΣΜΑΤΑ ΕΔΕΙΞΑΝ ΕΝΑΝ ΣΥΓΚΕΚΡΙΜΕΝΟ ΜΗΧΑΝΙΣΜΟ ΔΙΑΡΡΗΞΗΣ, ΣΤΟΝ ΟΠΟΙΟ ΜΙΑ ΣΥΖΗΓΙΑ ΔΙΑΣΤΑΥΡΟΜΕΝΩΝ ΖΩΝΩΝ ΟΡΙΖΟΝΤΙΩΝ ΔΙΑΤΜΗΤΙΚΩΝ ΤΑΣΕΩΝ, ΦΑΙΝΕΤΑΙ ΝΑ ΠΑΙΖΕΙ ΚΥΡΙΑΡΧΟ ΡΟΛΟ. Η ΣΥΝΔΙΑΣΜΕΝΗ ΔΕ ΔΡΑΣΗ ΤΩΝ ΖΩΝΩΝ ΑΥΤΩΝ ΟΔΗΓΕΙ ΣΤΗΝ ΔΙΑΝΟΙΞΗ ΤΟΠΙΚΩΝ ΕΓΚΑΡΣΙΩΝ ΛΕΚΑΝΩΝ ΔΙΑΣΤΟΛΗΣ

Dark Formation Detection using Recurrent Neural Networks and SAR Data

In this paper a classification scheme based on recurrent neural networks is presented. Neural networks may be viewed as a mathematical model composed of many non-linear computational elements, called neurons, operating in parallel and massively connected by links characterized by different weights. It is well known that conventional feedforward neural networks can be used to approximate any spatially finite function given a set of hidden nodes. Recurrent neural networks are fundamentally different from feedforward architectures in the sense that they not only operate on an input space but also on an internal state space – a trace of what already has been processed by the network. This capability is referred as internal memory of the recurrent networks. The general objectives of this paper are to describe, demonstrate and test the potential of simple recurrent artificial neural networks for dark formation detection using SAR satellite images over the sea surface. The type and the architecture of the network are subjects of research. Input to the networks is the original SAR image. The network is called to classify the image into dark formations and clean sea. Elman’s and Jordan’s recurrent networks have been examined. Jordan’s networks have been recognized as more suitable for dark formation detection. The Jordan’s specific architecture with five inputs, three hidden neurons and one output is proposed for dark formation detection as it classifies correctly more than 95.5% of the data set.JRC.G.6-Sensors, radar technologies and cybersecurit

An Object-Oriented Methodology to Detect Oil Spills

A new automated methodology for oil spill detection is presented, by which full synthetic aperture radar (SAR) high-resolution image scenes can be processed. The methodology relies on the object-oriented approach and profits from image segmentation techniques to detected dark formations. The detection of dark formations is based on a threshold definition that is fully adaptive to local contrast and brightness of large image segments. For the detection process, two empirical formulas are developed that also permit the classification of oil spills according to their brightness. A fuzzy classification method is used to classify dark formations as oil spills or look-alikes. Dark formations are not isolated and features of both dark areas and sea environment are considered. Various sea environments that affect oil spill shape and boundaries are grouped in two knowledge bases, used for the classification of dark formations. The accuracy of the method for the 12 SAR images used is 99.5% for the class of oil spills, and 98.8% for that of look-alikes. Fresh oil spills, fresh spills affected by natural phenomena, oil spills without clear stripping, small linear oil spills, oil spills with broken parts and amorphous oil spills can be successfully detected.JRC.G.4-Maritime affair

A New Object-oriented Methodology to Detect Oil Spills using ENVISAT Images

Several ASAR images from ENVISAT were tested for oil spill detection using a new object oriented approach. A new automated methodology for oil spill detection were previously introduced, by which full SAR high resolution image scenes can be processed. In the present paper the method is tested using full high resolution ENVISAT data. The methodology relies on the object oriented approach and profits of image segmentation techniques in order for dark formations to be detected. The detection of dark formations is based on a threshold definition which is fully adaptive to local contrast and brightness of large image segments. For the detection process, two empirical formulas were developed, which also permit the classification of oil spills according to their brightness. A fuzzy classification method is used to classify dark formations to oils spill or look-alikes. Dark formations are not isolated and features of both dark areas and sea environment are considered. Various sea environments which affect oil spill shape and boundaries are grouped in two knowledge bases, used for the classification of dark formations. The method’s accuracy was tested for ENVISAT images. Previously test for 12 ERS images saw more than 99% for oil spill accuracy, and close to 99% for look-alike accuracy. Fresh oil spills, fresh spills affected by natural phenomena, oil spills without clear stripping, small linear oil spills, oil spills with broken parts and amorphous oil spills can be successfully detected.JRC.G.4-Maritime affair

Potentiality of Feed-forward Neural Networks for Classifying Dark Formations to Oil Spills and Look-alikes

Radar backscatter values from oil spills are very similar to backscatter values from very calm sea areas and other ocean phenomena. Several studies aiming at oil spill detection have been conducted. Most of these studies rely on the detection of dark areas, which have high Bayesian probability of being oil spills. The drawback of these methods is a complex process, mainly because non-linearly separable datasets are introduced in statistically based decisions. The use of neural networks (NNs) in remote sensing has increased significantly, as NNs can simultaneously handle non-linear data of a multidimensional input space. In this article, we investigate the ability of two commonly used feed-forward NN models: multilayer perceptron (MLP) and radial basis function (RBF) networks, to classify dark formations in oil spills and look-alike phenomena. The appropriate training algorithm, type and architecture of the optimum network are subjects of research. Inputs to the networks are the original synthetic aperture radar image and other images derived from it. MLP networks are recognized as more suitable for oil spill detection.JRC.G.4-Maritime affair

Detection and Discrimination between Oil Spills and Look-Alike Phenomena through Neural Networks

Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in the marine environment, as their recording is independent of clouds and weather. Dark formations can be caused by man made actions (e.g. oil spill discharging) or natural ocean phenomena (e.g. natural slicks, wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they damp the capillary and short gravity sea waves. The ability of neural networks to detect dark formations in high resolution SAR images and to discriminate oil spills from lookalike phenomena simultaneously was examined. Two different neural networks are used; one to detect dark formations and the second one to perform a classification to oil spills or look-alikes. The proposed method is very promising in detecting dark formations and discriminating oil spills from look-alikes as it detects with an overall accuracy of 94% the dark formations and discriminate correctly 89% of examined cases.JRC.G.4-Maritime affair

Dark Formation Detection Using Neural Networks

Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in marine environment, as they are not affected by local weather conditions and cloudiness. Dark formations can be caused by man-made actions (e.g. oil spills) or natural ocean phenomena (e.g. natural slicks and wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they dampen the capillary and short gravity sea waves. Thus, traditionally, dark formation detection is the first stage of the oil-spill detection procedure and in most studies is performed manually or using a fixed size window in which a threshold value is adopted. In high-resolution imagery, dark formation detection may fail due to the nonlinear behaviour of the pixel values contained in the dark formation and in the area around it. In this paper, we examine the ability of two feed-forward neural network families, i.e. Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) networks, to detect dark formations in high-resolution SAR images. The general objective of this paper is to test the potential of artificial neural networks for dark formation detection using SAR high-resolution satellite images. Both the type and the architecture of the network are subjects of research. The inputs into the networks are the original SAR images. Each network is called to classify an area of the image as dark area or sea. The group of MLP networks can be recognized as the most suitable group for dark formation detection, as it presents reliable stable results for all the examined accuracies. Nevertheless, in terms of single topology, there is no an MLP topology that performs significantly better than the others.JRC.G.4-Maritime affair

Integrating Spaceborne SAR Imagery into Operational Systems for Fisheries Monitoring.

Abstract not availableJRC.(ISIS)-Institute For Systems, Informatics And Safet

On the Monitoring of Illicit Vessel Discharges. A Reconnaissance Study in the Mediterranean Sea.

Abstract not availableJRC.(ISIS)-Institute For Systems, Informatics And Safet