Brevissima storia dell'ottica

L'ottica è la parte della Fisica che studia la luce e i fenomeni legati alla sua propagazione ed interazione con la materia. Non essendo questo l'ambito per una discussione dei problemi epistemologici connessi all'ottica, discuteremo brevemente i fondamenti storico-sceintifici di questa disciplina ed alcune delle correlazioni fra l'ottica e le altre branche della fisica

A New automatic system of cell colony counting

The counting process of cell colonies is always a long and laborious process that is dependent on the judgment and ability of the operator. The judgment of the operator in counting can vary in relation to fatigue. Moreover, since this activity is time consuming it can limit the usable number of dishes for each experiment. For these purposes, it is necessary that an automatic system of cell colony counting is used. This article introduces a new automatic system of counting based on the elaboration of the digital images of cellular colonies grown on petri dishes. This system is mainly based on the algorithms of region-growing for the recognition of the regions of interest (ROI) in the image and a Sanger neural net for the characterization of such regions. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and confronted with the K-Nearest Neighbour (K-NN) and a Linear Discriminative Function (LDF). The preliminary results are shown

Imaging spectroscopic performances for a Si based detection system

We present the imaging and spectroscopic capabilities of a system based on a single photon counting chip (PCC) bump-bonded on a Si pixel detector. The system measures the energy spectrum and the flux, produced by a standard mammographic tube. We have also made some images of low contrast details, achieving good results

Low contrast imaging with a GaAs pixel digital detector

A digital mammography system based on a GaAs pixel detector has been developed by the INFN (Istituto Nazionale di Fisica Nucleare) collaboration MED46. The high atomic number makes the GaAs a very efficient material for low energy X-ray detection (10-30 keV is the typical energy range used in mammography). Low contrast details can be detected with a significant dose reduction to the patient. The system presented in this paper consists of a 4096 pixel matrix built on a 200 μm thick semi-insulating GaAs substrate. The pixel size is 170×170 μm2 for a total active area of 1.18 cm2 . The detector is bump-bonded to a VLSI front-end chip which implements a single-photon counting architecture. This feature allows to enhance the radiographic contrast detection with respect to charge integrating devices. The system has been tested by using a standard mammographic tube. Images of mammographic phantoms will be presented and compared with radiographs obtained with traditional film/screen systems. Monte Carlo simulations have been also performed to evaluate the imaging capability of the system. Comparison with simulations and experimental results will be shown

Evidence for non-exponential elastic proton-proton differential cross-section at low |t| and sqrt(s) = 8 TeV by TOTEM

The TOTEM experiment has made a precise measurement of the elastic proton-proton differential cross-section at the centre-of-mass energy sqrt(s) = 8 TeV based on a high-statistics data sample obtained with the beta* = 90 optics. Both the statistical and systematic uncertainties remain below 1%, except for the t-independent contribution from the overall normalisation. This unprecedented precision allows to exclude a purely exponential differential cross-section in the range of four-momentum transfer squared 0.027 < |t| < 0.2 GeV^2 with a significance greater than 7 sigma. Two extended parametrisations, with quadratic and cubic polynomials in the exponent, are shown to be well compatible with the data. Using them for the differential cross-section extrapolation to t = 0, and further applying the optical theorem, yields total cross-section estimates of (101.5 +- 2.1) mb and (101.9 +- 2.1) mb, respectively, in agreement with previous TOTEM measurements.Comment: Final version published in Nuclear Physics

Dissimilarity Application in Digitized Mammographic Images Classification

Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, an alternative ways can be found by constructing decision rules on dissimilarity (distance) representations. In such a recognition process a new object is described by its distances to (a subset of) the training samples. The use of the dissimilarities is especially of interest when features are difficult to obtain or when they have a little discriminative power. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features extracted from co-occurrence matrix containing spatial statistics information on ROI pixel grey tones. A dissimilarity representation of these features is made before the classification. A feed-forward neural network is employed to distinguish pathological records, from non-pathological ones by the new features. The results obtained in terms of sensitivity and specificity will be presented

Quantitative phase retrieval with picosecond X-ray pulses from the ATF Inverse Compton Scattering source

Quantitative phase retrieval is experimentally demonstrated using the Inverse Compton Scattering X-ray source available at the Accelerator Test Facility (ATF) in the Brookhaven National Laboratory. Phase-contrast images are collected using in-line geometry, with a single X-ray pulse of approximate duration of one picosecond. The projected thickness of homogeneous samples of various polymers is recovered quantitatively from the time-averaged intensity of transmitted X-rays. The data are in good agreement with the expectations showing that ATF Inverse Compton Scattering source is suitable for performing phase-sensitive quantitative X-ray imaging on the picosecond scale. The method shows promise for quantitative imaging of fast dynamic phenomena

Voxel-based Monte Carlo simulation of x-ray imaging and spectroscopy experiments

A Monte Carlo code for the simulation of X-ray imaging and spectroscopy experiments in heterogeneous samples is presented. The energy spectrum, polarization and profile of the incident beam can be defined so that X-ray tube systems as well as synchrotron sources can be simulated. The sample is modeled as a 3D regular grid. The chemical composition and density is given at each point of the grid. Photoelectric absorption, fluorescent emission, elastic and inelastic scattering are included in the simulation. The core of the simulation is a fast routine for the calculation of the path lengths of the photon trajectory intersections with the grid voxels. The voxel representation is particularly useful for samples that cannot be well described by a small set of polyhedra. This is the case of most naturally occurring samples. In such cases, voxel-based simulations are much less expensive in terms of computational cost than simulations on a polygonal representation. The efficient scheme used for calculating the path lengths in the voxels and the use of variance reduction techniques make the code suitable for the detailed simulation of complex experiments on generic samples in a relatively short time. Examples of applications to X-ray imaging and spectroscopy experiments are discussed

Automatic cell colony counting by region-growing approach

This paper introduces a new automatic system of counting based on the elaboration of the digital images of cellular colonies grown on petri dishes. This system is mainly based on the region-growing algorithms for the recognition of the Regions Of Interest (ROI) in the image and Sanger’s neural network for the characterization of such regions. Moreover a recognition of the most important filters is made in alternative respect to region-growing approach. The new Graphics Users Interface is introduced. The better final classification is supplied from a Feed- Forward Neural Net (FF-NN) and compared with the K-Nearest Neighbour (K-NN). The results on large dataset of ROIs are shown