Exploring the knowledge contained in neuroimages: Statistical discriminant analysis and automatic segmentation of the most significant changes

Objective: The aim of this article is to propose an integrated framework for extracting and describing patterns of disorders from medical images using a combination of linear discriminant analysis and active contour models. Methods: A multivariate statistical methodology was first used to identify the most discriminating hyperplane separating two groups of images (from healthy controls and patients with schizophrenia) contained in the input data. After this, the present work makes explicit the differences found by the multivariate statistical method by subtracting the discriminant models of controls and patients, weighted by the pooled variance between the two groups. A variational level-set technique was used to segment clusters of these differences. We obtain a label of each anatomical change using the Talairach atlas. Results: In this work all the data was analysed simultaneously rather than assuming a priori regions of interest. As a consequence of this, by using active contour models, we were able to obtain regions of interest that were emergent from the data. The results were evaluated using, as gold standard, well-known facts about the neuroanatomical changes related to schizophrenia. Most of the items in the gold standard was covered in our result set. Conclusions: We argue that such investigation provides a suitable framework for characterising the high complexity of magnetic resonance images in schizophrenia as the results obtained indicate a high sensitivity rate with respect to the gold standard. (C) 2010 Elsevier B.V. All rights reserved.

Modelo de data mining para deteção de embolias pulmonares

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Estimation of 3D shapes using active surface models

This paper addresses the estimation of surfaces from a set of 3D points using the unified framework described in [1]. This framework proposes the use of competitive learning for curve estimation, i.e., a set of points is defined on a deformable curve and they all compete to represent the available data. This paper extends the use of the unified framework to surface estimation. It o shown that competitive learning performes better than snakes, improving the model performance in the presence of concavities and allowing to desciminate close surfaces. The proposed model is evaluated in this paper using syntheticdata and medical images (MRI and ultrasound images).

Integração modular em visualizadores médicos e aposição da marcação CE do Dispositivo Médico

Neste documento ´e feita a descrição detalhada da integração modular de um script no software OsiriX. O objectivo deste script ´e determinar o diâmetro central da artéria aorta a partir de uma Tomografia Computorizada. Para tal são abordados conceitos relacionados com a temática do processamento de imagem digital, tecnologias associadas, e.g., a norma DICOM e desenvolvimento de software. Como estudo preliminar, são analisados diversos visualizadores de imagens médica, utilizados para investigação ou mesmo comercializados. Foram realizadas duas implementações distintas do plugin. A primeira versão do plugin faz a invocação do script de processamento usando o ficheiro de estudo armazenado em disco; a segunda versão faz a passagem de dados através de um bloco de memória partilhada e utiliza o framework Java Native Interface. Por fim, é demonstrado todo o processo de aposição da Marcação CE de um dispositivo médico de classe IIa e obtenção da declaração de conformidade por parte de um Organismo Notificado. Utilizaram-se os Sistemas Operativos Mac OS X e Linux e as linguagens de programação Java, Objective-C e Python.

Metodologia a seguir per la reconstrucció d’imatges mèdiques i la seva transformació en geometries mallades per a l’ús computacional

Estudi elaborat a partir d’una estada a l'Imperial College of London, Gran Bretanya, entre setembre i desembre 2006. Disposar d'una geometria bona i ben definida és essencial per a poder resoldre eficientment molts dels models computacionals i poder obtenir uns resultats comparables a la realitat del problema. La reconstrucció d'imatges mèdiques permet transformar les imatges obtingudes amb tècniques de captació a geometries en formats de dades numèriques . En aquest text s'explica de forma qualitativa les diverses etapes que formen el procés de reconstrucció d'imatges mèdiques fins a finalment obtenir una malla triangular per a poder‐la processar en els algoritmes de càlcul. Aquest procés s'inicia a l'escàner MRI de The Royal Brompton Hospital de Londres del que s'obtenen imatges per a després poder‐les processar amb les eines CONGEN10 i SURFGEN per a un entorn MATLAB. Aquestes eines les han desenvolupat investigadors del Bioflow group del departament d'enginyeria aeronàutica del Imperial College of London i en l'ultim apartat del text es comenta un exemple d'una artèria que entra com a imatge mèdica i surt com a malla triangular processable amb qualsevol programari o algoritme que treballi amb malles.

Desenvolupament d'una plataforma web per a integrar serveis de neuroimatge al PIC (Port d'Informació Científica)

El projecte ha consistit en el disseny i implementació d'una arquitectura/plataforma d'integració dels serveis d'emmagatzemament i postprocessament d'imatge mèdica que oferix el grup així com la visualització, anonimització, transferència d'arxius... basat en una interfície web com a frontend de la plataforma. Els servis que requereixen interacció gràfica han estat implementats mitjançant tècniques d'exportació d'escriptori remotament a la web i altres s'han implementat per tal que funcionin amb el cluster de màquines del que disposa el PIC.

BIMS: Biomedical Information Management System

This final year project presents the design principles and prototype implementation of BIMS (Biomedical Information Management System), a flexible software system which provides an infrastructure to manage all information required by biomedical research projects.The BIMS project was initiated with the motivation to solve several limitations in medical data acquisition of some research projects, in which Universitat Pompeu Fabra takes part. These limitations,based on the lack of control mechanisms to constraint information submitted by clinicians, impact on the data quality, decreasing it.BIMS can easily be adapted to manage information of a wide variety of clinical studies, not being limited to a given clinical specialty. The software can manage both, textual information, like clinical data (measurements, demographics, diagnostics, etc ...), as well as several kinds of medical images (magnetic resonance imaging, computed tomography, etc ...). Moreover, BIMS provides a web - based graphical user interface and is designed to be deployed in a distributed andmultiuser environment. It is built on top of open source software products and frameworks.Specifically, BIMS has been used to represent all clinical data being currently used within the CardioLab platform (an ongoing project managed by Universitat Pompeu Fabra), demonstratingthat it is a solid software system, which could fulfill requirements of a real production environment.

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.

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

Desenvolupament d'interfícies d'usuari i visualització d'imatge mèdica en dispositius mòbils Android

Aquest treball tracta sobre la realització d’una aplicació per a dispositius Android que permeti visualitzar imatges mèdiques. Amb la col·laboració del parc Taulí de Sabadell s’han realitzat tasques de recerca de requeriments com l’elaboració d’una entrevista o la realització d’una maqueta de paper que s’ha sotmès a una avaluació heurística en un procés iteratiu. Finalment, s’ha implementat una aplicació on es poden visualitzar imatges mèdiques, fer accions sobre elles com canviar el color o fer zoom i accedir a informació del pacient.

Articulated Model Registration of MRI/X-Ray Spine Data

This paper presents a method based on articulated models for the registration of spine data extracted from multimodal medical images of patients with scoliosis. With the ultimate aim being the development of a complete geometrical model of the torso of a scoliotic patient, this work presents a method for the registration of vertebral column data using 3D magnetic resonance images (MRI) acquired in prone position and X-ray data acquired in standing position for five patients with scoliosis. The 3D shape of the vertebrae is estimated from both image modalities for each patient, and an articulated model is used in order to calculate intervertebral transformations required in order to align the vertebrae between both postures. Euclidean distances between anatomical landmarks are calculated in order to assess multimodal registration error. Results show a decrease in the Euclidean distance using the proposed method compared to rigid registration and more physically realistic vertebrae deformations compared to thin-plate-spline (TPS) registration thus improving alignment.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

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 often associated 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 microcalcifications is 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 microcalcifications have 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 sense of adding new features not only related to the shape

Modelo tridimensional del tendón del músculo supraespinoso

Introducción: Teniendo en cuenta el envejecimiento de la población y la alta prevalencia de las lesiones del manguito rotador no es de extrañar que esta patología se convierta en un problema de salud pública. Se sabe que el aumento en el tamaño de una lesión se asocia con la aparición de síntomas, pero no existen herramientas que permitan predecir la evolución del tamaño de una lesión. Con esto en mente se desarrollo una línea de investigación para estudiar el mecanismo de falla que inicia con la realización de un modelo tridimensional de un tendón del musculo supraespinoso sano. Materiales y métodos: Se caracterizo el tendón del músculo supraespinoso aplicando cargas uniaxiales a 7 complejos humero-tendón-escápula cadavéricos. Con los datos obtenidos se alimento un modelo tridimensional lineal isotrópico analizando la concentración de esfuerzos de von Misses Resultados: Del ensayo uniaxial se obtuvieron curvas esfuerzo-deformación homogéneas para el 20% de la deformación inicial, obteniendo un modulo de Young (14.4±2.3MPa) y un coeficiente de Poisson (0.14) con una concentración de esfuerzos de en la zona central de la cara articular del tendón, cercana a su inserción. Encontramos una disminución del 5% en los esfuerzos al retirar el acromion del modelo. Conclusiones: Se caracterizó de manera exitosa y se obtuvo un modelo tridimensional del tendón. La distribución de esfuerzos es compatible con la reportada en la literatura. El acromion no tiene mayor importancia en la magnitud de los esfuerzos en nuestro modelo. Este es el punto de partida para estudiar el mecanismo de falla.

A non-parametric vessel detection method for complex vascular structures

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the Most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions. (C) 2008 Elsevier B.V. All rights reserved.