Tuning Iris Recognition for Noisy Images

The use of iris recognition for human authentication has been spreading in the past years. Daugman has proposed a method for iris recognition, composed by four stages: segmentation, normalization, feature extraction, and matching. In this paper we propose some modifications and extensions to Daugman's method to cope with noisy images. These modifications are proposed after a study of images of CASIA and UBIRIS databases. The major modification is on the computationally demanding segmentation stage, for which we propose a faster and equally accurate template matching approach. The extensions on the algorithm address the important issue of pre-processing that depends on the image database, being mandatory when we have a non infra-red camera, like a typical WebCam. For this scenario, we propose methods for reflection removal and pupil enhancement and isolation. The tests, carried out by our C# application on grayscale CASIA and UBIRIS images show that the template matching segmentation method is more accurate and faster than the previous one, for noisy images. The proposed algorithms are found to be efficient and necessary when we deal with non infra-red images and non uniform illumination.

Evaluation of the respiratory motion effect in small animal PET images with GATE Monte Carlo simulations

The rapid growth in genetics and molecular biology combined with the development of techniques for genetically engineering small animals has led to increased interest in in vivo small animal imaging. Small animal imaging has been applied frequently to the imaging of small animals (mice and rats), which are ubiquitous in modeling human diseases and testing treatments. The use of PET in small animals allows the use of subjects as their own control, reducing the interanimal variability. This allows performing longitudinal studies on the same animal and improves the accuracy of biological models. However, small animal PET still suffers from several limitations. The amounts of radiotracers needed, limited scanner sensitivity, image resolution and image quantification issues, all could clearly benefit from additional research. Because nuclear medicine imaging deals with radioactive decay, the emission of radiation energy through photons and particles alongside with the detection of these quanta and particles in different materials make Monte Carlo method an important simulation tool in both nuclear medicine research and clinical practice. In order to optimize the quantitative use of PET in clinical practice, data- and image-processing methods are also a field of intense interest and development. The evaluation of such methods often relies on the use of simulated data and images since these offer control of the ground truth. Monte Carlo simulations are widely used for PET simulation since they take into account all the random processes involved in PET imaging, from the emission of the positron to the detection of the photons by the detectors. Simulation techniques have become an importance and indispensable complement to a wide range of problems that could not be addressed by experimental or analytical approaches.

Convex Total Variation Denoising of Poisson Fluorescence Confocal Images With Anisotropic Filtering

Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm. A comparison with several state-of-the-art algorithms is also presented.

Otimização do co-registo metabolismo-morfologia em estudos de PET/CT com 18F-DG de nódulos pulmonares: comparação de dois protocolos - 4D vs CT lento

Mestrado em Medicina Nuclear - Área de especialização: Tomografia por Emissão de Positrões.

Análise dos parâmetros de qualidade das imagens de verificação em radioterapia aplicada a patologias de cabeça e pescoço

Mestrado em Radiações Aplicadas às Tecnologias da Saúde - Área de especialização: Terapia com Radiações.

Denoising of LSFCM images with compensation for the photoblinking/photobleaching effects

Fluorescence confocal microscopy images present a low signal to noise ratio and a time intensity decay due to the so called photoblinking and photobleaching effects. These effects, together with the Poisson multiplicative noise that corrupts the images, make long time biological observation processes very difficult.

Efeito fisiológico da cafeína em estudos neurológicos com base na ponderação em susceptibilidade ferromagnética (SWI)

Introdução – O presente estudo avaliou o efeito da cafeína no valor da razão contraste ruído (CNR) em imagens SWI. Objetivos – Avaliar o efeito da cafeína qualitativamente e quantificado pelo cálculo do valor CNR em imagens de magnitude e MIP para as estruturas: veia cerebral interna, seio sagital superior, tórcula e artéria cerebral média. Metodologia – A população do estudo incluiu 24 voluntários saudáveis que estiveram pelo menos 24h privados da ingestão de cafeína. Adquiriram-se imagens SWI antes e após a ingestão de 100ml de café. Os voluntários foram subdivididos em quatro grupos de seis indivíduos/grupo e avaliados separadamente após decorrido um intervalo de tempo diferente para cada grupo (15, 25, 30 ou 45min pós-cafeína). Utilizou-se um scanner Siemens Avanto 1,5 T com bobine standard de crânio e os parâmetros: T2* GRE 3D de alta resolução no plano axial, TR=49; TE=40; FA=15; FOV=187x230; matriz=221x320. O processamento de imagem foi efetuado no software OsiriX® e a análise estatística no GraphPadPrism®. Resultados e Discussão – As alterações de sinal e diferenças de contraste predominaram nas estruturas venosas e não foram significantes na substância branca, LCR e artéria cerebral média. Os valores CNR pré-cafeína diferiram significativamente do pós-cafeína nas imagens de magnitude e MIP na veia cerebral interna e nas imagens de magnitude do seio sagital superior e da tórcula (p<0,0001). Não se verificaram diferenças significativas entre os grupos avaliados nos diferentes tempos pós-cafeína. Conclusões – Especulamos que a cafeína possa vir a ser usada como agente de contraste nas imagens SWI barato, eficaz e de fácil administração.

Mapeamento das regiões cerebrais associadas ao engano

Mestrado em Radiações aplicadas às Tecnologias da Saúde - Ramo de Ressonância Magnética

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.

Projeto: “Olha um trator!”

Descobrir o meio significa ter oportunidade de o observar, de o sentir, de o viver, de o experienciar, em suma, de interagir com ele. As crianças pequenas, quando estimuladas a descobrir o meio que as rodeia, desenvolvem um conjunto de competências que lhes permite a construção de imagens sobre o mundo nas suas múltiplas dimensões. O projeto que apresentamos surgiu de um percurso rotineiro pela área envolvente ao Jardim de Infância. Tem a particularidade de se desenvolver em meio rural, num contexto facilitador de experiências vivas e plenas de significado para as crianças. Percorrendo de forma integrada diferentes áreas de conteúdo, mas privilegiando o Conhecimento do Mundo, a Expressão Dramática e o Desenvolvimento Pessoal e Social, este projeto, centrado na descoberta do trator agrícola, procura no meio e na comunidade envolvente os recursos que o fazem crescer e que o tornam tão significativo para as crianças que nele se envolvem: os espaços, onde se constrói e partilha o conhecimento; os profissionais, a quem se colocam todas as questões e onde se procura ajuda para a realização das tarefas mais difíceis; as famílias, com quem se vive diariamente todo o processo.

Non-enzymatic assay for glucose by using immobilized whole-cells of E. coli containing glucose binding protein fused to fluorescent proteins

Glucose monitoring in vivo is a crucial issue for gaining new understanding of diabetes. Glucose binding protein (GBP) fused to two fluorescent indicator proteins (FLIP) was used in the present study such as FLIP-glu- 3.2 mM. Recombinant Escherichia coli whole-cells containing genetically encoded nanosensors as well as cell-free extracts were immobilized either on inner epidermis of onion bulb scale or on 96-well microtiter plates in the presence of glutaraldehyde. Glucose monitoring was carried out by Förster Resonance Energy Transfer (FRET) analysis due the cyano and yellow fluorescent proteins (ECFP and EYFP) immobilized in both these supports. The recovery of these immobilized FLIP nanosensors compared with the free whole-cells and cell-free extract was in the range of 50–90%. Moreover, the data revealed that these FLIP nanosensors can be immobilized in such solid supports with retention of their biological activity. Glucose assay was devised by FRET analysis by using these nanosensors in real samples which detected glucose in the linear range of 0–24 mM with a limit of detection of 0.11 mM glucose. On the other hand, storage and operational stability studies revealed that they are very stable and can be re-used several times (i.e. at least 20 times) without any significant loss of FRET signal. To author's knowledge, this is the first report on the use of such immobilization supports for whole-cells and cell-free extract containing FLIP nanosensor for glucose assay. On the other hand, this is a novel and cheap high throughput method for glucose assay.