Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

FMRI 3D registration based on Fourier space subsets using neural networks

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

3D-2D image registration by nonlinear regression

We propose a 3D-2D image registration method that relates image features of 2D projection images to the transformation parameters of the 3D image by nonlinear regression. The method is compared with a conventional registration method based on iterative optimization. For evaluation, simulated X-ray images (DRRs) were generated from coronary artery tree models derived from 3D CTA scans. Registration of nine vessel trees was performed, and the alignment quality was measured by the mean target registration error (mTRE). The regression approach was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach.

Integração e contextualização de modelos 3D no ensino da área da saúde

Mestrado em Engenharia Informática. Sistemas Gráficos e Multimédia.

Modelo generalista de páginas Web para motores de pesquisa com interface 3D

Web tornou-se uma ferramenta indispensável para a sociedade moderna. A capacidade de aceder a enormes quantidades de informação, disponível em praticamente todo o mundo, é uma grande vantagem para as nossas vidas. No entanto, a quantidade avassaladora de informação disponível torna-se um problema, que é o de encontrar a informação que precisamos no meio de muita informação irrelevante. Para nos ajudar nesta tarefa, foram criados poderosos motores de pesquisa online, que esquadrinham a Web à procura dos melhores resultados, segundo os seus critérios, para os dados que precisamos. Actualmente, os motores de pesquisa em voga, usam um formato de apresentação de resultados simples, que consiste apenas numa caixa de texto para o utilizador inserir as palavras-chave sobre o tema que quer pesquisar e os resultados são dispostos sobre uma lista de hiperligações ordenada pela relevância que o motor atribui a cada resultado. Porém, existem outras formas de apresentar resultados. Uma das alternativas é apresentar os resultados sobre interfaces em 3 dimensões. É nestes tipos de sistemas que este trabalho vai incidir, os motores de pesquisa com interfaces em 3 dimensões. O problema é que as páginas Web não estão preparadas para serem consumidas por este tipo de motores de pesquisa. Para resolver este problema foi construído um modelo generalista para páginas Web, que consegue alimentar os requisitos das diversas variantes destes motores de pesquisa. Foi também desenvolvido um protótipo de instanciação automático, que recolhe as informações necessárias das páginas Web e preenche o modelo.

Caracterização e modelação de fissuras em edifícios utilizando 3D laser scanning, com vista à sua reabilitação

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil

Visualização e manipulação de informação em ambientes 3D imersivos

Ainda antes da invenção da escrita, o desenho foi utilizado para descrever a realidade, tendo evoluído ao longo dos tempos, ganhando mais qualidade e pormenor e recorrendo a suportes cada vez mais evoluídos que permitissem a perpetuação dessa imagem: dessa informação. Desde as pinturas rupestres, nas paredes de grutas paleolíticas, passando pelos hieróglifos, nos templos egípcios, nas gravuras das escrituras antigas e nos quadros sobre tela, a intenção sempre foi a de transmitir a informação da forma mais directa e perceptível por qualquer indivíduo. Nos dias de hoje as novas tecnologias permitem aceder à informação com uma facilidade nunca antes vista ou imaginada, estando certamente ainda por descobrir outras formas de registar e perpetuar a informação para as gerações vindouras. A fotografia está na origem das grandes evoluções da imagem, permitindo capturar o momento, tornando-o “eterno”. Hoje em dia, na era da imagem digital, além de se mostrar a realidade, é possível incorporar na imagem informação adicional, de modo a enriquecer a experiência de visualização e a maximizar a aquisição do conhecimento. As possibilidades da visualização em três dimensões (3D) vieram dar o realismo que faltava ao formato de fotografia original. O 3D permite a imersão do espectador no ambiente que, a própria imagem retrata, à qual se pode ainda adicionar informação escrita ou até sensorial como, por exemplo, o som. Esta imersão num ambiente tridimensional permite ao utilizador interagir com a própria imagem através da navegação e exploração de detalhes, usando ferramentas como o zoom ou ligações incorporados na imagem. A internet é o local onde, hoje em dia, já se disponibilizam estes ambientes imersivos, tornando esta experiência muita mais acessível a qualquer pessoa. Há poucos anos ainda, esta prática só era possível mediante o recurso a dispositivos especificamente construídos para o efeito e que, por isso, apenas estavam disponíveis a grupos restritos de utilizadores. Esta dissertação visa identificar as características de um ambiente 3D imersivo e as técnicas existentes e possíveis de serem usadas para maximizar a experiência de visualização. Apresentar-se-ão algumas aplicações destes ambientes e sua utilidade no nosso dia-a-dia, antevendo as tendências futuras de evolução nesta área. Serão apresentados exemplos de ferramentas para a composição e produção destes ambientes e serão construídos alguns modelos ilustrativos destas técnicas, como forma de avaliar o esforço de desenvolvimento e o resultado obtido, comparativamente com formas mais convencionais de transmitir e armazenar a informação. Para uma avaliação mais objectiva, submeteram-se os modelos produzidos à apreciação de diversos utilizadores, a partir da qual foram elaboradas as conclusões finais deste trabalho relativamente às potencialidades de utilização de ambientes 3D imersivos e suas mais diversas aplicações.

Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization

Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.

Helical tomotherapy versus 3D conformal radiotherapy in dosimetric planning for patients with localized prostate cancer

Radiotherapy is one of the therapeutics selected for localized prostate cancer, in cases where the tumour is confined to the prostate, penetrates the prostatic capsule or has reached the seminal vesicles (T1 to T3 stages). The radiation therapy can be administered through various modalities, being historically used the 3D conformal radiotherapy (3DCRT). Other modality of radiation administration is the intensity modulated radiotherapy (IMRT), that allows an increase of the total dose through modulation of the treatment beams, enabling a reduction in toxicity. One way to administer IMRT is through helical tomotherapy (TH). With this study we intent to analyze the advantages of helical tomotherapy when compared with 3DCRT, by evaluating the doses in the organs at risk (OAR) and planning target volumes (PTV).

A reliable M-mode ultrasound protocol for the assessment of diaphragm motion

Diaphragm is the principal inspiratory muscle. Different techniques have been used to assess diaphragm motion. Among them, M-mode ultrasound has gain particular interest since it is non-invasive and accessible. However it is operator-dependent and no objective acquisition protocol has been established. Purpose: to establish a reliable method for the assessment of the diaphragmatic motion via the M-mode ultrasound.

Fatty liver automatic diagnosis from ultrasound images

In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of the method is the utilization of the speckle noise that corrupts the ultrasound images to compute textural features of the liver parenchyma relevant for the diagnosis. The algorithm uses the Bayesian framework to compute a noiseless image, containing anatomic and echogenic information of the liver and a second image containing only the speckle noise used to compute the textural features. The classification results, with the Bayes classifier using manually classified data as ground truth show that the automatic classifier reaches an accuracy of 95% and a 100% of sensitivity.

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.

Smart object for 3D interaction

This paper reports on the creation of an interface for 3D virtual environments, computer-aided design applications or computer games. Standard computer interfaces are bound to 2D surfaces, e.g., computer mouses, keyboards, touch pads or touch screens. The Smart Object is intended to provide the user with a 3D interface by using sensors that register movement (inertial measurement unit), touch (touch screen) and voice (microphone). The design and development process as well as the tests and results are presented in this paper. The Smart Object was developed by a team of four third-year engineering students from diverse scientific backgrounds and nationalities during one semester.