Eye-tracking of nodule detection in lung CT volumetric data.

PURPOSE: Signal detection on 3D medical images depends on many factors, such as foveal and peripheral vision, the type of signal, and background complexity, and the speed at which the frames are displayed. In this paper, the authors focus on the speed with which radiologists and naïve observers search through medical images. Prior to the study, the authors asked the radiologists to estimate the speed at which they scrolled through CT sets. They gave a subjective estimate of 5 frames per second (fps). The aim of this paper is to measure and analyze the speed with which humans scroll through image stacks, showing a method to visually display the behavior of observers as the search is made as well as measuring the accuracy of the decisions. This information will be useful in the development of model observers, mathematical algorithms that can be used to evaluate diagnostic imaging systems. METHODS: The authors performed a series of 3D 4-alternative forced-choice lung nodule detection tasks on volumetric stacks of chest CT images iteratively reconstructed in lung algorithm. The strategy used by three radiologists and three naïve observers was assessed using an eye-tracker in order to establish where their gaze was fixed during the experiment and to verify that when a decision was made, a correct answer was not due only to chance. In a first set of experiments, the observers were restricted to read the images at three fixed speeds of image scrolling and were allowed to see each alternative once. In the second set of experiments, the subjects were allowed to scroll through the image stacks at will with no time or gaze limits. In both static-speed and free-scrolling conditions, the four image stacks were displayed simultaneously. All trials were shown at two different image contrasts. RESULTS: The authors were able to determine a histogram of scrolling speeds in frames per second. The scrolling speed of the naïve observers and the radiologists at the moment the signal was detected was measured at 25-30 fps. For the task chosen, the performance of the observers was not affected by the contrast or experience of the observer. However, the naïve observers exhibited a different pattern of scrolling than the radiologists, which included a tendency toward higher number of direction changes and number of slices viewed. CONCLUSIONS: The authors have determined a distribution of speeds for volumetric detection tasks. The speed at detection was higher than that subjectively estimated by the radiologists before the experiment. The speed information that was measured will be useful in the development of 3D model observers, especially anthropomorphic model observers which try to mimic human behavior.

CHARACTERISING THE EOS SLOT-SCANNING SYSTEM WITH THE EFFECTIVE DETECTIVE QUANTUM EFFICIENCY.

NlmCategory="UNASSIGNED">As opposed to the standard detective quantum efficiency (DQE), effective DQE (eDQE) is a figure of merit that allows comparing the performances of imaging systems in the presence of scatter rejection devices. The geometry of the EOS™ slot-scanning system is such that the detector is self-collimated and rejects scattered radiation. In this study, the EOS system was characterised using the eDQE in imaging conditions similar to those used in clinical practice: with phantoms of different widths placed in the X-ray beam, for various incident air kerma and tube voltages corresponding to the phantom thickness. Scatter fractions in EOS images were extremely low, around 2 % for all configurations. Maximum eDQE values spanned 9-14.8 % for a large range of air kerma at the detector plane from 0.01 to 1.34 µGy. These figures were obtained with non-optimised EOS setting but still over-performed most of the maximum eDQEs recently assessed for various computed radiology and digital radiology systems with antiscatter grids.

Microcalcification evaluation in computer assisted diagnosis for digital mammography

In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is oftenassociated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcificationsis performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcificationshave been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the senseof adding new features not only related to the shape

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Gamma spectrometry and gamma and X-ray tomography of nuclear fuel

The purpose of gamma spectrometry and gamma and X-ray tomography of nuclear fuel is to determine both radionuclide concentration and integrity and deformation of nuclear fuel. The aims of this thesis have been to find out the basics of gamma spectrometry and tomography of nuclear fuel, to find out the operational mechanisms of gamma spectrometry and tomography equipment of nuclear fuel, and to identify problems that relate to these measurement techniques. In gamma spectrometry of nuclear fuel the gamma-ray flux emitted from unstable isotopes is measured using high-resolution gamma-ray spectroscopy. The production of unstable isotopes correlates with various physical fuel parameters. In gamma emission tomography the gamma-ray spectrum of irradiated nuclear fuel is recorded for several projections. In X-ray transmission tomography of nuclear fuel a radiation source emits a beam and the intensity, attenuated by the nuclear fuel, is registered by the detectors placed opposite. When gamma emission or X-ray transmission measurements are combined with tomographic image reconstruction methods, it is possible to create sectional images of the interior of nuclear fuel. MODHERATO is a computer code that simulates the operation of radioscopic or tomographic devices and it is used to predict and optimise the performance of imaging systems. Related to the X-ray tomography, MODHERATO simulations have been performed by the author. Gamma spectrometry and gamma and X-ray tomography are promising non-destructive examination methods for understanding fuel behaviour under normal, transient and accident conditions.

Computational Techniques for Spectral Image Analysis

Multispectral images are becoming more common in the field of remote sensing, computer vision, and industrial applications. Due to the high accuracy of the multispectral information, it can be used as an important quality factor in the inspection of industrial products. Recently, the development on multispectral imaging systems and the computational analysis on the multispectral images have been the focus of a growing interest. In this thesis, three areas of multispectral image analysis are considered. First, a method for analyzing multispectral textured images was developed. The method is based on a spectral cooccurrence matrix, which contains information of the joint distribution of spectral classes in a spectral domain. Next, a procedure for estimating the illumination spectrum of the color images was developed. Proposed method can be used, for example, in color constancy, color correction, and in the content based search from color image databases. Finally, color filters for the optical pattern recognition were designed, and a prototype of a spectral vision system was constructed. The spectral vision system can be used to acquire a low dimensional component image set for the two dimensional spectral image reconstruction. The data obtained by the spectral vision system is small and therefore convenient for storing and transmitting a spectral image.

Low dark count geiger mode avalanche photodiodes fabricated in conventional CMOS technologies

Avalanche photodiodes operated in the Geiger mode present very high intrinsic gain and fast time response, which make the sensor an ideal option for those applications in which detectors with high sensitivity and velocity are required. Moreover, they are compatible with conventional CMOS technologies, allowing sensor and front-end electronics integration within the pixel cell. Despite these excellent qualities, the photodiode suffers from high intrinsic noise, which degrades the performance of the detector and increases the memory area to store the total amount of information generated. In this work, a new front-end circuit that allows low reverse bias overvoltage sensor operation to reduce the noise in Geiger mode avalanche photodiode pixel detectors is presented. The proposed front-end circuit also enables to operate the sensor in the gated acquisition mode to further reduce the noise. Experimental characterization of the fabricated pixel with the conventional HV-AMS 0.35µm technology is also presented in this article.

Confocal and two-photon microscopy : image enhancement /

Confocal and two-photon microcopy have become essential tools in biological research and today many investigations are not possible without their help. The valuable advantage that these two techniques offer is the ability of optical sectioning. Optical sectioning makes it possible to obtain 3D visuahzation of the structiu-es, and hence, valuable information of the structural relationships, the geometrical, and the morphological aspects of the specimen. The achievable lateral and axial resolutions by confocal and two-photon microscopy, similar to other optical imaging systems, are both defined by the diffraction theorem. Any aberration and imperfection present during the imaging results in broadening of the calculated theoretical resolution, blurring, geometrical distortions in the acquired images that interfere with the analysis of the structures, and lower the collected fluorescence from the specimen. The aberrations may have different causes and they can be classified by their sources such as specimen-induced aberrations, optics-induced aberrations, illumination aberrations, and misalignment aberrations. This thesis presents an investigation and study of image enhancement. The goal of this thesis was approached in two different directions. Initially, we investigated the sources of the imperfections. We propose methods to eliminate or minimize aberrations introduced during the image acquisition by optimizing the acquisition conditions. The impact on the resolution as a result of using a coverslip the thickness of which is mismatched with the one that the objective lens is designed for was shown and a novel technique was introduced in order to define the proper value on the correction collar of the lens. The amoimt of spherical aberration with regard to t he numerical aperture of the objective lens was investigated and it was shown that, based on the purpose of our imaging tasks, different numerical apertures must be used. The deformed beam cross section of the single-photon excitation source was corrected and the enhancement of the resolution and image quaUty was shown. Furthermore, the dependency of the scattered light on the excitation wavelength was shown empirically. In the second part, we continued the study of the image enhancement process by deconvolution techniques. Although deconvolution algorithms are used widely to improve the quality of the images, how well a deconvolution algorithm responds highly depends on the point spread function (PSF) of the imaging system applied to the algorithm and the level of its accuracy. We investigated approaches that can be done in order to obtain more precise PSF. Novel methods to improve the pattern of the PSF and reduce the noise are proposed. Furthermore, multiple soiu'ces to extract the PSFs of the imaging system are introduced and the empirical deconvolution results by using each of these PSFs are compared together. The results confirm that a greater improvement attained by applying the in situ PSF during the deconvolution process.

Computational prediction of neural progenitor cell fates

Understanding how stem and progenitor cells choose between alternative cell fates is a major challenge in developmental biology. Efforts to tackle this problem have been hampered by the scarcity of markers that can be used to predict cell division outcomes. Here we present a computational method, based on algorithmic information theory, to analyze dynamic features of living cells over time. Using this method, we asked whether rat retinal progenitor cells (RPCs) display characteristic phenotypes before undergoing mitosis that could foretell their fate. We predicted whether RPCs will undergo a self-renewing or terminal division with 99% accuracy, or whether they will produce two photoreceptors or another combination of offspring with 87% accuracy. Our implementation can segment, track and generate predictions for 40 cells simultaneously on a standard computer at 5 min per frame. This method could be used to isolate cell populations with specific developmental potential, enabling previously impossible investigations.

Sistema informàtic d’ajut a la detecció i al seguiment del melanoma

Mitjançant les tècniques de visió per computador aquest projecte pretén desenvolupar una aplicació capaç de segmentar la pell, detectar nevus (pigues i altres taques) i poder comparar imatges de pacients amb risc de contreure melanoma preses en moments diferents. Aquest projecte pretén oferir diferents eines informàtiques als dermatòlegs per a propòsits relacionats amb la investigació. L’ objectiu principal d’ aquest projecte és desenvolupar un sistema informàtic que proporcioni als dermatòlegs agilitat a l’hora de gestionar les dades dels pacients amb les sevesimatges corresponents, ajudar-los en la realització de deteccions dels nevus d’aquestes imatges, i ajudar-los en la comparació d’exploracions (amb les deteccions realitzades)de diferents èpoques d’un mateix pacient

Mòdul de visualització 3D d’ ajuda al diagnòstic de les neoplàsies rectals

El diagnòstic mitjançant la imatge mèdica s’ha convertit en una eina fonamental en la pràctica clínica, permet entre altres coses, reconstruir a partir d’un conjunt d’imatges 2D, obtingudes a partir d’aparells de captació, qualsevol part de l’organisme d’un pacient i representar-lo en un model 3D. Sobre aquest model 3D poden realitzar-se diferents operacions que faciliten el diagnòstic i la presa de decisions als especialistes. El projecte que es presenta forma part del desenvolupament de la plataforma informàtica de visualització i tractament de dades mèdiques, anomenada Starviewer, que desenvolupen conjuntament el laboratori de Gràfics i Imatge (GiLab) de la Universitat de Girona i l’ Institut de Diagnòstic per la Imatge (IDI) de l’Hospital Josep Trueta de Girona. En particular, en aquest projecte es centra en el diagnòstic del càncer colorectal i el desenvolupament de mètodes i tècniques de suport al seu diagnòstic. Els dos punts claus en el tractament d’aqueta patologia són: la detecció de les lesions I l’estudi de l’evolució d’aquestes lesions, una vegada s’ha iniciat el tractament tumoral. L’objectiu principal d’aquest projecte és implementar i integrar en la plataforma Starviewer les tècniques de visualització i processament de dades necessàries per donar suport als especialistes en el diagnòstic de les lesions del colon. Donada la dificultat en el processament de les dades reals del budell ens proposem: dissenyar i implementar un sistema per crear models sintètics del budell; estudiar, implementar i avaluar les tècniques de processament d’imatge que calen per segmentar lesions de budell; dissenyar i implementar un sistema d’exploració del budell iintegrar de tots els mòduls implementats en la plataforma starviewer

Visualització de models fusionats

L’objectiu principal d’aquest projecte era implementar la visualització 3D de models fusionats i aplicar totes les tècniques possibles per realitzar aquesta fusió. Aquestes tècniques s’integraran en la plataforma de visualització i processament de dades mèdiques STARVIEWER. Per assolir l’ objectiu principal s’ han definit els següents objectius específics:1- estudiar els algoritmes de visualització de models simples i analitzar els diferents paràmetres a tenir en compte. 2- ampliació de la tècnica de visualització bàsica seleccionada per tal de suportar els models fusionats. 3- avaluar i compar tots els mètodes implementats per poder determinar quin ofereix les millors visualitzacions

Integració d’un visualitzador de volums en una plataforma de reconstrucció de mapes de fibres del cervell

Estudi, disseny i implementació d’un algorisme de visualització de volums i integrar-lo en la plataforma DTIWeb de visualització i processament de dades de DTI. La plataforma DTIWeb és una plataforma desenvolupada conjuntament entre el Laboratori de Gràfics i Imatge de la Universitat de Girona i d’Institut de Diagnòstic per la imatge de l’Hospital Josep Trueta de Girona. Aquesta plataforma integra els mètodes bàsics de reconstrucció de fibres del cervell. La principal limitació de la plataforma és que no suporta la visualització de models 3D. Aquest fet limita el seu us en la pràctica clínica habitual ja que es fa difícil la interpretació dels mapes de connectivitat que genera