A Fast General-Purpose Clustering Algorithm Based on FPGAs for High-Throughput Data Processing

We present a fast general-purpose algorithm for high-throughput clustering of data "with a two dimensional organization". The algorithm is designed to be implemented with FPGAs or custom electronics. The key feature is a processing time that scales linearly with the amount of data to be processed. This means that clustering can be performed in pipeline with the readout, without suffering from combinatorial delays due to looping multiple times through all the data. This feature makes this algorithm especially well suited for problems where the data has high density, e.g. in the case of tracking devices working under high-luminosity condition such as those of LHC or Super-LHC. The algorithm is organized in two steps: the first step (core) clusters the data; the second step analyzes each cluster of data to extract the desired information. The current algorithm is developed as a clustering device for modern high-energy physics pixel detectors. However, the algorithm has much broader field of applications. In fact, its core does not specifically rely on the kind of data or detector it is working for, while the second step can and should be tailored for a given application. Applications can thus be foreseen to other detectors and other scientific fields ranging from HEP calorimeters to medical imaging. An additional advantage of this two steps approach is that the typical clustering related calculations (second step) are separated from the combinatorial complications of clustering. This separation simplifies the design of the second step and it enables it to perform sophisticated calculations achieving online-quality in online applications. The algorithm is general purpose in the sense that only minimal assumptions on the kind of clustering to be performed are made.Comment: 11th Frontier Detectors For Frontier Physics conference (2009

An automatic system to discriminate malignant from benign massive lesions in mammograms

Evaluating the degree of malignancy of a massive lesion on the basis of the mere visual analysis of the mammogram is a non-trivial task. We developed a semi-automated system for massive-lesion characterization with the aim to support the radiological diagnosis. A dataset of 226 masses has been used in the present analysis. The system performances have been evaluated in terms of the area under the ROC curve, obtaining A_z=0.80+-0.04.Comment: 4 pages, 2 figure; Proceedings of the Frontier Science 2005, 4th International Conference on Frontier Science, 12-17 September, 2005, Milano, Ital

An automated system for lung nodule detection in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The results obtained on the collected database of low-dose thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 9 pages, 9 figures; Proceedings of the SPIE Medical Imaging Conference, 17-22 February 2007, San Diego, California, USA, Vol. 6514, 65143

A Theoretical Prediction of the Bs-Meson Lifetime Difference

We present the results of a quenched lattice calculation of the operator matrix elements relevant for predicting the Bs width difference. Our main result is (\Delta\Gamma_Bs/\Gamma_Bs)= (4.7 +/- 1.5 +/- 1.6) 10^(-2), obtained from the ratio of matrix elements, R(m_b)=/<\bar B_s^0|Q_L|B_s^0>=-0.93(3)^(+0.00)_(-0.01). R(m_b) was evaluated from the two relevant B-parameters, B_S^{MSbar}(m_b)=0.86(2)^(+0.02)_(-0.03) and B_Bs^{MSbar}(m_b) = 0.91(3)^(+0.00)_(-0.06), which we computed in our simulation.Comment: 21 pages, 7 PostScript figure

Computer-aided detection of pulmonary nodules in low-dose CT

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical CT images with 1.25 mm slice thickness is being developed in the framework of the INFN-supported MAGIC-5 Italian project. The basic modules of our lung-CAD system, a dot enhancement filter for nodule candidate selection and a voxel-based neural classifier for false-positive finding reduction, are described. Preliminary results obtained on the so-far collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the CompIMAGE - International Symposium on Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications, 20-21 Oct. 2006, Coimbra, Portuga

A scalable system for microcalcification cluster automated detection in a distributed mammographic database

A computer-aided detection (CADe) system for microcalcification cluster identification in mammograms has been developed in the framework of the EU-founded MammoGrid project. The CADe software is mainly based on wavelet transforms and artificial neural networks. It is able to identify microcalcifications in different datasets of mammograms (i.e. acquired with different machines and settings, digitized with different pitch and bit depth or direct digital ones). The CADe can be remotely run from GRID-connected acquisition and annotation stations, supporting clinicians from geographically distant locations in the interpretation of mammographic data. We report and discuss the system performances on different datasets of mammograms and the status of the GRID-enabled CADe analysis.Comment: 6 pages, 4 figures; Proceedings of the IEEE NNS and MIC Conference, October 23-29, 2005, Puerto Ric

Algoritmi paralleli per l'allineamento di immagini

In questa tesi si propone un algoritmo che, date una serie di immagini astronomiche raffiguranti uno stesso soggetto, cerca di ricavarne una rappresentazione migliore. Scattare foto astronomiche non è affatto facile: bisogna seguire vari accorgimenti e regolare opportunamente le impostazioni della propria fotocamera. In genere gli astrofili, che non sempre dispongono di sofisticatissime attrezzature per fotografare lo spazio, scattano una serie di foto o registrano un video (da cui estrarranno i fotogrammi) del soggetto che vogliono immortalare, attraverso la lente di un telescopio: in questa maniera riescono a catturare dettagli invisibili all'occhio umano, ma le immagini ottenute presentano parecchio rumore; per questo motivo le astrofotografie sono quasi sempre sottoposte ad una fase di elaborazione. Una semplice procedura per ridurre il rumore consiste nel produrre una nuova immagine i cui pixel abbiano valore pari alla media dei corrispettivi pixel di tutte le immagini scattate. Dato che la tecnica di cattura precedentemente descritta è enormemente sensibile allo spostamento, prima di calcolare la media dei pixel, è necessario allineare le immagini, rimuovendo lo scostamento presente tra esse. Per effettuare questo procedimento (noto anche come image registration) esistono varie tecniche più o meno complicate: in questa tesi ne verranno presentate brevemente alcune, ma ci si limiterà ad implementarne una versione banale, basata su una tecnica di brute force. La tematica affrontata si presta molto bene alla programmazione parallela, perciò l'implementazione dell'algoritmo che verrà proposto sfrutterà le clausole OpenMP e le istruzioni SIMD di un Raspberry Pi 2 B; è stata scelta tale architettura date le notevoli capacità di parallelizzazione che è in grado di offrire nonostante i ridotti costi, le contenute dimensioni e la minima energia richiesta

GPCALMA: a Grid Approach to Mammographic Screening

The next generation of High Energy Physics experiments requires a GRID approach to a distributed computing system and the associated data management: the key concept is the "Virtual Organisation" (VO), a group of geographycally distributed users with a common goal and the will to share their resources. A similar approach is being applied to a group of Hospitals which joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), which will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. HEP techniques come into play in writing the application code, which makes use of neural networks for the image analysis and shows performances similar to radiologists in the diagnosis. GRID technologies will allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays presently associated to screening programs.Comment: 4 pages, 3 figures; to appear in the Proceedings of Frontier Detectors For Frontier Physics, 9th Pisa Meeting on Advanced Detectors, 25-31 May 2003, La Biodola, Isola d'Elba, Ital

A Computer-Aided Detection system for lung nodules in CT images

Lung cancer is the leading cause of cancer-related mortality in developed countries. To support radiologists in the identification of early-stage lung cancers, we propose a Computer-Aided Detection (CAD) system, composed by two different procedures: VBNACADI devoted to the identification of small nodules embedded in the lung parenchyma (internal nodules) and VBNACADJP devoted the identification of nodules originating on the pleura surface (juxta-pleural nodules). The CAD system has been developed and tested on a dataset of low-dose and thin-slice CT scans collected in the framework of the first Italian randomized and controlled screening trial (ITALUNG-CT). This work has been carried out in the framework of MAGIC-5 (Medical Application on a Grid Infrastructure Connection), an Italian collaboration funded by Istituto Nazionale di Fisica Nucleare (INFN) and Ministero dell’Universit`a e della Ricerca (MIUR), which aims at developing models and algorithms for a distributed analysis of biomedical images, by making use of the GRID services