Marco Cipolla

Multi-Scale Analysis of the European Airspace Using Network Community Detection

We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.

Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of a CAD (Computer Aided Detection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%).

An island strategy for memetic discrete tomography reconstruction

In this paper we present a parallel island model memetic algorithm for binary discrete tomography reconstruction that uses only four projections without any further a priori information. The underlying combination strategy consists in separated populations of agents that evolve by means of different processes. Agents progress towards a possible solution by using genetic operators, switch and a particular compactness operator. A guided migration scheme is applied to select suitable migrants by considering both their own and their sub-population fitness. That is, from time to time, we allow some individuals to transfer to different subpopulations. The benefits of this paradigm were tested in terms of correctness, robustness and time of the reconstruction by considering publicly available datasets of images. To tackle the so-called stability problem, we considered the case of noisy projections along four directions to simulate an instrumental error. Results show that the proposed method decreases the reconstruction error for all classes of images with respect to a serial implementation recently proposed by the authors, and that such reconstruction error is almost invariant with respect to the number of demes. Moreover, the computation time of the proposed parallel memetic algorithm scales in a quasi-linear manner with respect to the demes number, and is invariant with respect to the used number of migrations.

Comparative Study of Human and Automated Screening for Antinuclear Antibodies by Immunofluorescence on HEp-2 Cells

Background : Several automated systems had been developed in order to reduce inter-observer variability in indirect immunofluorescence (IIF) interpretation. We aimed to evaluate the performance of a processing system in antinuclear antibodies (ANA) screening on HEp-2 cells. Patients and Methods : This study included 64 ANA-positive sera and 107 ANA-negative sera that underwent IIF on two commercial kits of HEp-2 cells (BioSystems® and Euroimmun®). IIF results were compared with a novel automated interpretation system, the “ Cyclopus CADImmuno®” (CAD). Results : All ANA-positive sera images were recognized as positive by CAD (sensitivity = 100%), while 17 (15.9%) of the ANA-negative sera images were interpreted as positive (specificity = 84.1%), κ=0.799 (SD=0.045). Comparison of IIF pattern determination between human and CAD system revealed on HEp-2 (BioSystems®), a complete concordance in 6 (9.37%) sera, a partial concordance (sharing of at least 1 pattern) in 42 (65.6%) cases and in 16 (25%) sera the pattern interpretation was discordant. Similarly, on HEp-2 (Euroimmun®) the concordance in pattern interpretation was total in 5 (7.8%) sera, partial in 39 (60.9%) and absent in 20 (31.25%). For both tested HEp-2 cells kits agreement was enhanced for the most common patterns, homogenous, fine speckled and coarse speckled. While there was an issue in identification of nucleolar, dots and nuclear membranous patterns by CAD. Conclusion : Assessment of ANA by IIF on HEp-2 cells using the automated interpretation system, the “ Cyclopus CADImmuno®” is a reliable method for positive/negative differentiation. Continuous integration of IIF images would improve the pattern identification by the CAD.

A memetic island model for discrete tomography reconstruction

Soft computing is a term indicating a coalition of methodologies, and its basic dogma is that, in general, better results can be obtained through the use of constituent methodologies in combination, rather than in a stand alone mode. Evolutionary computing belongs to this coalition, and thus memetic algorithms. Here, we present a combination of several instances of a recently proposed memetic algorithm for discrete tomography reconstruction, based on the island model parallel implementation. The combination is motivated by the fact that, even though the results of the recently proposed approach are finally better and more robust compared to other approaches, we advised that its major drawback was the computational time. The underlying combination strategy consists in separated populations of agents evolving by means of different processes which share some individuals, from time to time. Experiments were performed to test the benefits of this paradigm in terms of computational time and correctness of the solutions.

Genetic normalized convolution

Normalized convolution techniques operate on very few samples of a given digital signal and add missing information, trough spatial interpolation. From a practical viewpoint, they make use of data really available and approximate the assumed values of the missing information. The quality of the final result is generally better than that obtained by traditional filling methods as, for example, bilinear or bicubic interpolations. Usually, the position of the samples is assumed to be random and due to transmission errors of the signal. Vice versa, we want to apply normalized convolution to compress data. In this case, we need to arrange a higher density of samples in proximity of zones which contain details, with respect to less significant, uniform parts of the image. This paper describes an evolutionary approach to evaluate the position of certain samples, in order to reconstruct better images, according to a subjective definition of visual quality. An extensive analysis on real data was carried out to verify the correctness of the proposed methodology.

An Evolution of the Non-Parameter Harris Affine Corner Detector: A Distributed Approach

A parallel version of a new automatic Harris-based corner detector is presented. A scheduler to dynamically and homogeneously distribute high computational workload on heterogeneous parallel architectures such as Grid systems has been implemented to speedup the whole procedure. Experimental results show the robustness of the underlying scheduler, which can be easily exploited in various automatic image analysis systems.

An Automatic Three-Dimensional Fuzzy Edge Detector

Three-dimensional object analysis is of particular interest in many research fields. In this context, the most common data representation is boundary mesh, namely, 2D surface embedded in 3D space. We will investigate the problem of 3D edge extraction, that is, salient surface regions characterized by high flexure. Our automatic edge detection method assigns a value, proportional to the local bending of the surface, to the elements of the mesh. Moreover, a proper scanning window, centered on each element, is used to discriminate between smooth zones of the surface and its edges. The algorithm does not require input parameters and returns a set of elements that represent the salient features on the model surface. This method is general enough, returns representative structures of the object, as edges, and can be considered as a pre-processing step for further applications, such as 3D compact representation, matching and recognition.