Solitary pulmonary nodule and 18F-FDG PET/CT. Part 1: epidemiology, morphological evaluation and cancer probability

Abstract Solitary pulmonary nodule corresponds to a common radiographic finding, which is frequently detected incidentally. The investigation of this entity remains complex, since characteristics of benign and malignant processes overlap in the differential diagnosis. Currently, many strategies are available to evaluate solitary pulmonary nodules with the main objective of characterizing benign lesions as best as possible, while avoiding to expose patients to the risks inherent to invasive methods, besides correctly detecting cases of lung cancer so as the potential curative treatment is not delayed. This first part of the study focuses on the epidemiology, the morfological evaluation and the methods to determine the likelihood of cancer in cases of indeterminate solitary pulmonary nodule.

Analysis of translational errors in frame-based and frameless cranial radiosurgery using an anthropomorphic phantom

Abstract Objective: To evaluate three-dimensional translational setup errors and residual errors in image-guided radiosurgery, comparing frameless and frame-based techniques, using an anthropomorphic phantom. Materials and Methods: We initially used specific phantoms for the calibration and quality control of the image-guided system. For the hidden target test, we used an Alderson Radiation Therapy (ART)-210 anthropomorphic head phantom, into which we inserted four 5mm metal balls to simulate target treatment volumes. Computed tomography images were the taken with the head phantom properly positioned for frameless and frame-based radiosurgery. Results: For the frameless technique, the mean error magnitude was 0.22 ± 0.04 mm for setup errors and 0.14 ± 0.02 mm for residual errors, the combined uncertainty being 0.28 mm and 0.16 mm, respectively. For the frame-based technique, the mean error magnitude was 0.73 ± 0.14 mm for setup errors and 0.31 ± 0.04 mm for residual errors, the combined uncertainty being 1.15 mm and 0.63 mm, respectively. Conclusion: The mean values, standard deviations, and combined uncertainties showed no evidence of a significant differences between the two techniques when the head phantom ART-210 was used.

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed

Security Authentication using Phase-Encoded Nanoparticle Structures and Polarized Light

Phase encoded nano structures such as Quick Response (QR) codes made of metallic nanoparticles are suggested to be used in security and authentication applications. We present a polarimetric optical method able to authenticate random phase encoded QR codes. The system is illuminated using polarized light and the QR code is encoded using a phase-only random mask. Using classification algorithms it is possible to validate the QR code from the examination of the polarimetric signature of the speckle pattern. We used Kolmogorov-Smirnov statistical test and Support Vector Machine algorithms to authenticate the phase encoded QR codes using polarimetric signatures.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Avaliação quimiométrica da distribuição de metais pesados em composto de lixo urbano domiciliar

The main obstacle to the use of compost from urban waste in agriculture is the presence of heavy metals. Once in the soil, their effect is accumulative and they may contaminate crops and water. The present study reports the evaluation of the chemical distributions of Cu, Pb, Mn and Zn in three different sized fractions (unsieved, < 1,18mm and > 1,18mm) of compost, by means of a sequencial extraction procedure and a chemometric analysis of the total content of all metals in each fraction. The pattern recognition methods showed significant differences in total heavy metal contents for the different fractions. The finest one was the most contaminated. Meanwhile, this fraction presented lower amounts of metals in avaliable forms. This behavior can be attributed to the presence of metal particles in their elemental states in this fraction.

Digitaalisen sisältöliiketoiminnan ja viestinnän klusterin synty ja menestystekijät

Kiinnostus digitaalista sisältöliiketoimintaa ja viestintää kohtaan on kasvanut suuresti viimeisten kymmenen vuoden aikana. Niin alan yritysmäärä kuin tuotteet ovat lisääntyneet entisestään viestinnän siirtyessä yhä enemmän tietoverkkoihin. Myös Suomen valtion ja Euroopan unionin tasolla tähän kehitykseen on reagoitu voimakkaasti eri ohjelmien ja hankkeiden kautta. Suomi on tehnyt kolmannen tietoyhteiskuntastrategiansa ja Euroopan unionillakin on laadittuna omat strategiansa ja tavoitteensa tuleville vuosille. Kouvolan kaupunki on nostanut digitaalisen sisältöliiketoiminnan ja viestinnän yhdeksi tulevaisuuden kärkialueikseen, minkä vuoksi tämä tutkimus on tilattu. Tutkimuksessa selvitettiin digiklusterin nykytilaa, mahdollisuuksia ja menestystekijöitä. Tutkimusmenetelminä käytettiin syvähaastatteluita avaintoimijoille sekä vertailua aiemmin menestyneisiin ICT- ja sisältöliiketoiminnan klustereihin. Tutkimuksessa perehdytään ensin aihealueeseen teoriatiedon ja aiempien tutkimustulosten kautta. Tämän jälkeen tarkastellaan tutkittavaa aluetta tilastotiedon, hankkeiden ja haastatteluiden kautta. Lisäksi alueen osaamispääomaa on kartoitettu haastatteluiden ja selvitysten perusteella. Tutkimustuloksena havaittiin, että menestyäkseen klusterin tulee panostaa olemassa olevaan yrityskantaan sekä houkutella alueelle erityisesti kasvuvaiheen yrityksiä. Lisäksi kävi ilmi, että klusterikehitys on mahdollista vain yhteisten päämäärien ja tavoitteiden kautta. Mukana kehittämisessä tulee olla niin alueen yrittäjät kuin julkisen hallinnon edustajat. Kehitys onnistuu parhaiten, mikäli hankkeissa kiinnitetään huomiota yritysnäkökulmaan ja toimijat tietävät oman roolinsa. Korkean osaamisen klusterissa keskeisellä sijalla on alueen koulutus- ja tutkimuslaitosten, yritysten ja julkisen hallinnon saumaton yhteistyö.

New Distance Transforms for Gray Level Image Compression

This thesis deals with distance transforms which are a fundamental issue in image processing and computer vision. In this thesis, two new distance transforms for gray level images are presented. As a new application for distance transforms, they are applied to gray level image compression. The new distance transforms are both new extensions of the well known distance transform algorithm developed by Rosenfeld, Pfaltz and Lay. With some modification their algorithm which calculates a distance transform on binary images with a chosen kernel has been made to calculate a chessboard like distance transform with integer numbers (DTOCS) and a real value distance transform (EDTOCS) on gray level images. Both distance transforms, the DTOCS and EDTOCS, require only two passes over the graylevel image and are extremely simple to implement. Only two image buffers are needed: The original gray level image and the binary image which defines the region(s) of calculation. No other image buffers are needed even if more than one iteration round is performed. For large neighborhoods and complicated images the two pass distance algorithm has to be applied to the image more than once, typically 3 10 times. Different types of kernels can be adopted. It is important to notice that no other existing transform calculates the same kind of distance map as the DTOCS. All the other gray weighted distance function, GRAYMAT etc. algorithms find the minimum path joining two points by the smallest sum of gray levels or weighting the distance values directly by the gray levels in some manner. The DTOCS does not weight them that way. The DTOCS gives a weighted version of the chessboard distance map. The weights are not constant, but gray value differences of the original image. The difference between the DTOCS map and other distance transforms for gray level images is shown. The difference between the DTOCS and EDTOCS is that the EDTOCS calculates these gray level differences in a different way. It propagates local Euclidean distances inside a kernel. Analytical derivations of some results concerning the DTOCS and the EDTOCS are presented. Commonly distance transforms are used for feature extraction in pattern recognition and learning. Their use in image compression is very rare. This thesis introduces a new application area for distance transforms. Three new image compression algorithms based on the DTOCS and one based on the EDTOCS are presented. Control points, i.e. points that are considered fundamental for the reconstruction of the image, are selected from the gray level image using the DTOCS and the EDTOCS. The first group of methods select the maximas of the distance image to new control points and the second group of methods compare the DTOCS distance to binary image chessboard distance. The effect of applying threshold masks of different sizes along the threshold boundaries is studied. The time complexity of the compression algorithms is analyzed both analytically and experimentally. It is shown that the time complexity of the algorithms is independent of the number of control points, i.e. the compression ratio. Also a new morphological image decompression scheme is presented, the 8 kernels' method. Several decompressed images are presented. The best results are obtained using the Delaunay triangulation. The obtained image quality equals that of the DCT images with a 4 x 4

Analytical methods for vancomycin determination in biological fluids and in pharmaceuticals

Vancomycin is a glycopeptide antibiotic employed in the treatment of infections caused by certain methicillin-resistant staphylococci. It is indicated also for patients allergic to penicillin or when there is no response to penicillins or cephalosporins. The adequate vancomycin concentration levels in blood serum lies between 5 and 10 mg/L. Higher values are toxic, causing mainly nephrotoxicity and ototoxicity. Various analytical methods are described in the literature: spectrophotometric, immunologic, biologic and chromatographic methods. This paper reviews the main analytical methods for vancomycin determination in biological fluids and in pharmaceutical preparations.

Recomendações para calibração em Química Analítica parte 2: calibração multianalito

This paper is a translation of an IUPAC document by K. Danzer, M. Otto and L. A. Currie (Pure Appl. Chem., 2004, 76(6), 1215-1225). Its goal is to establish a uniform and meaningful standard for terminology (in Portuguese), notation, and formulation concerning multispecies calibration in analytical chemistry. Calibration in analytical chemistry refers to the relation between sample domain and measurement domain (signal domain) expressed by an analytical function x = f s (Q) representing a pattern of chemical species Q and their amounts or concentrations x in a given test sample and a measured function y = f (z) that may be a spectrum, chromatogram, etc. Simultaneous multispecies analyses are carried out mainly by spectroscopic and chromatographic methods in a more or less selective way. For the determination of n species Qi (i=1,2, ..., n), at least n signals must be measured which should be well separated in the ideal case. In analytical practice, the situation can be different.

Bowing the violin : a case study for auditory-motor pattern modelling in the context of music performance

This project addresses methodological and technological challenges in the development of multi-modal data acquisition and analysis methods for the representation of instrumental playing technique in music performance through auditory-motor patterning models. The case study is violin playing: a multi-modal database of violin performances has been constructed by recording different musicians while playing short exercises on different violins. The exercise set and recording protocol have been designed to sample the space defined by dynamics (from piano to forte) and tone (from sul tasto to sul ponticello), for each bow stroke type being played on each of the four strings (three different pitches per string) at two different tempi. The data, containing audio, video, and motion capture streams, has been processed and segmented to facilitate upcoming analyses. From the acquired motion data, the positions of the instrument string ends and the bow hair ribbon ends are tracked and processed to obtain a number of bowing descriptors suited for a detailed description and analysis of the bow motion patterns taking place during performance. Likewise, a number of sound perceptual attributes are computed from the audio streams. Besides the methodology and the implementation of a number of data acquisition tools, this project introduces preliminary results from analyzing bowing technique on a multi-modal violin performance database that is unique in its class. A further contribution of this project is the data itself, which will be made available to the scientific community through the repovizz platform.

Development and validation of RP-LC and uv spectrophotometric methods to assay bromopride in oral and injectable solutions

A reversed-phase liquid chromatographic (LC) and ultraviolet (UV) spectrophotometric methods were developed and validated for the assay of bromopride in oral and injectable solutions. The methods were validated according to ICH guideline. Both methods were linear in the range between 5-25 μg mL-1 (y = 41837x - 5103.4, r = 0.9996 and y = 0.0284x - 0.0351, r = 1, respectively). The statistical analysis showed no significant difference between the results obtained by the two methods. The proposed methods were found to be simple, rapid, precise, accurate, and sensitive. The LC and UV methods can be used in the routine quantitative analysis of bromopride in oral and injectable solutions.