Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

The structural connectivity of the brain is considered to encode species-wise and subject-wise patterns that will unlock large areas of understanding of the human brain. Currently, diffusion MRI of the living brain enables to map the microstructure of tissue, allowing to track the pathways of fiber bundles connecting the cortical regions across the brain. These bundles are summarized in a network representation called connectome that is analyzed using graph theory. The extraction of the connectome from diffusion MRI requires a large processing flow including image enhancement, reconstruction, segmentation, registration, diffusion tracking, etc. Although a concerted effort has been devoted to the definition of standard pipelines for the connectome extraction, it is still crucial to define quality assessment protocols of these workflows. The definition of quality control protocols is hindered by the complexity of the pipelines under test and the absolute lack of gold-standards for diffusion MRI data. Here we characterize the impact on structural connectivity workflows of the geometrical deformation typically shown by diffusion MRI data due to the inhomogeneity of magnetic susceptibility across the imaged object. We propose an evaluation framework to compare the existing methodologies to correct for these artifacts including whole-brain realistic phantoms. Additionally, we design and implement an image segmentation and registration method to avoid performing the correction task and to enable processing in the native space of diffusion data. We release PySDCev, an evaluation framework for the quality control of connectivity pipelines, specialized in the study of susceptibility-derived distortions. In this context, we propose Diffantom, a whole-brain phantom that provides a solution to the lack of gold-standard data. The three correction methodologies under comparison performed reasonably, and it is difficult to determine which method is more advisable. We demonstrate that susceptibility-derived correction is necessary to increase the sensitivity of connectivity pipelines, at the cost of specificity. Finally, with the registration and segmentation tool called regseg we demonstrate how the problem of susceptibility-derived distortion can be overcome allowing data to be used in their original coordinates. This is crucial to increase the sensitivity of the whole pipeline without any loss in specificity.

A review of current knowledge on Electronic Vision Enhancement Systems for the visually impaired

Magnification can be provided to assist those with visual impairment to make the best use of remaining vision. Electronic transverse magnification of an object was first conceived for use in low vision in the late 1950s, but has developed slowly and is not extensively prescribed because of its relatively high cost and lack of portability. Electronic devices providing transverse magnification have been termed closed-circuit televisions (CCTVs) because of the direct cable link between the camera imaging system and monitor viewing system, but this description generally refers to surveillance devices and does not indicate the provision of features such as magnification and contrast enhancement. Therefore, the term Electronic Vision Enhancement Systems (EVES) is proposed to better distinguish and describe such devices. This paper reviews current knowledge on EVES for the visually impaired in terms of: classification; hardware and software (development of technology, magnification and field-of-view, contrast and image enhancement); user aspects (users and usage, reading speed and duration, and training); and potential future development of EVES. © 2003 The College of Optometrists.

Dynamic Image Precompensation for Improving Visual Performance of Computer Users with Ocular Aberrations

With the progress of computer technology, computers are expected to be more intelligent in the interaction with humans, presenting information according to the user's psychological and physiological characteristics. However, computer users with visual problems may encounter difficulties on the perception of icons, menus, and other graphical information displayed on the screen, limiting the efficiency of their interaction with computers. In this dissertation, a personalized and dynamic image precompensation method was developed to improve the visual performance of the computer users with ocular aberrations. The precompensation was applied on the graphical targets before presenting them on the screen, aiming to counteract the visual blurring caused by the ocular aberration of the user's eye. A complete and systematic modeling approach to describe the retinal image formation of the computer user was presented, taking advantage of modeling tools, such as Zernike polynomials, wavefront aberration, Point Spread Function and Modulation Transfer Function. The ocular aberration of the computer user was originally measured by a wavefront aberrometer, as a reference for the precompensation model. The dynamic precompensation was generated based on the resized aberration, with the real-time pupil diameter monitored. The potential visual benefit of the dynamic precompensation method was explored through software simulation, with the aberration data from a real human subject. An "artificial eye'' experiment was conducted by simulating the human eye with a high-definition camera, providing objective evaluation to the image quality after precompensation. In addition, an empirical evaluation with 20 human participants was also designed and implemented, involving image recognition tests performed under a more realistic viewing environment of computer use. The statistical analysis results of the empirical experiment confirmed the effectiveness of the dynamic precompensation method, by showing significant improvement on the recognition accuracy. The merit and necessity of the dynamic precompensation were also substantiated by comparing it with the static precompensation. The visual benefit of the dynamic precompensation was further confirmed by the subjective assessments collected from the evaluation participants.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Evaluation of the maxillary premolar roots dissociation using radiographic holders with conventional and digital radiography

This in vivo study evaluated the dissociation quality of maxillary premolar roots combining variations of vertical and horizontal angulations by using X-ray holders (Rinn -XCP), and made a comparison between two types of intraoral radiography systems - conventional film (Kodak Insight, Rochester, USA) and digital radiography (Kodak RVG 6100, Kodak, Rochester, USA). The study sample was comprised of 20 patients with a total of 20 maxillary premolars that were radiographed, using the paralleling angle technique (GP), with a 20º variation of the horizontal angle (GM) and 25º variation of the horizontal angle combined with 15º vertical angle (GMV). Each image was independently analyzed by two experienced examiners. These examiners assigned a score to the diagnostic capability of root dissociation and the measurement of the distance between the apexes. Statistical data was derived using the Wilcoxon Signed Rank test, Friedman and T test. The means of the measured distances between buccal and lingual root apexes were greater for the GMV, which ranged from 2.3 mm to 3.3 mm. A statistically significant difference was found between GM and GMV when compared to GP with p < 0.01. An established best diagnostic dissociation roots image was found in the GMV. These results support the use of the anterior X-ray holders which offer a better combined deviation (GMV) to dissociate maxillary premolar roots in both radiography systems.

Resultados preliminares de um algoritmo para ablação de lente de contato personalizada

OBJETIVO: Desenvolver simulação computadorizada de ablação para produzir lentes de contato personalizadas a fim de corrigir aberrações de alta ordem. MÉTODOS: Usando dados reais de um paciente com ceratocone, mensurados em um aberrômetro ("wavefront") com sensor Hartmann-Shack, foram determinados as espessuras de lentes de contato que compensam essas aberrações assim como os números de pulsos necessários para fazer ablação as lentes especificamente para este paciente. RESULTADOS: Os mapas de correção são apresentados e os números dos pulsos foram calculados, usando feixes com a largura de 0,5 mm e profundidade de ablação de 0,3 µm. CONCLUSÕES: Os resultados simulados foram promissores, mas ainda precisam ser aprimorados para que o sistema de ablação "real" possa alcançar a precisão desejada.

A performance social nas organizações

No âmbito deste trabalho proponho-me elaborar um estudo sobre o conceito da Responsabilidade Social Empresarial no setor das telecomunicações, e ilustrar algumas das vantagens inerentes à adoção de um comportamento empresarial socialmente responsável, que contribui por sua vez para o entendimento, comunicação, divulgação e crescimento da estratégia empresarial neste setor. Procurarei expor, que a estratégia de RSE, sendo acolhida voluntariamente e recebendo o apoio de todos stakeholders poderá gerar um reforço da imagem, reputação, distinção da concorrência (devido à criação do produto social com valor) e confiança do consumidor sendo algumas das vantagens identificadas e peças fundamentais para o sucesso de uma empresa. Propus-me analisar o caso de estudo das empresas PT, TIM, Telefónica, América Móvil e China Mobile. O principal objetivo deste estudo consiste em compreender a performance das empresas anteriores, relativamente às ações de responsabilidade social, na dimensão social.

Results of radiation protection programmes on mammography

In this paper, we present the results of mammography quality control tests related to the work with Portuguese mammography equipment, either in conventional or in digital mammography computed radiography, showing the main differences in the tested equipments. Quality control in mammography is a very special area of quality control in radiology, which demands relatively high knowledge on physics. Digital imaging is changing the standards of the radiographic imaging. Regarding mammography, this is yet a controversial issue owing to some limitations of the digital detectors, like the resolution for instance. A complete set of results regarding radiation protection of the patients submitted to mammography diagnosis is presented. A discussion of the quality image parameters and its interpretation in conventional and digital mammography is presented. In conclusion, we present a sample of results that can be considered as characteristics of mammography equipment in Portugal.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Calibration, selection and mosaicing of SMART-1 AMIE images

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

High-resolution magnetic resonance imaging quantitatively detects individual pancreatic islets.

OBJECTIVE-We studied whether manganese-enhanced high-field magnetic resonance (MR) imaging (MEHFMRI) could quantitatively detect individual islets in situ and in vivo and evaluate changes in a model of experimental diabetes.RESEARCH DESIGN AND METHODS-Whole pancreata from untreated (n = 3), MnCl(2) and glucose-injected mice (n = 6), and mice injected with either streptozotocin (STZ; n = 4) or citrate buffer (n = 4) were imaged ex vivo for unambiguous evaluation of islets. Exteriorized pancreata of MnCl(2) and glucose-injected mice (n = 6) were imaged in vivo to directly visualize the gland and minimize movements. In all cases, MR images were acquired in a 14.1 Testa scanner and correlated with the corresponding (immuno)histological sections.RESULTS-In ex vivo experiments, MEHFMRI distinguished different pancreatic tissues and evaluated the relative abundance of islets in the pancreata of normoglycemic mice. MEHFMRI also detected a significant decrease in the numerical and volume density of islets in STZ-injected mice. However, in the latter measurements the loss of beta-cells was undervalued under the conditions tested. The experiments on the externalized pancreata confirmed that MEHFMRI could visualize native individual islets in living, anesthetized mice.CONCLUSIONS-Data show that MEHFMRI quantitatively visualizes individual islets in the intact mouse pancreas, both ex vivo and in vivo. Diabetes 60:2853-2860, 2011

Renal perfusion evaluation with contrast-enhanced ultrasonography.

BACKGROUND: Contrast-enhanced ultrasonography (CEUS) is a novel imaging technique that is safe and applicable on the bedside. Recent developments seem to enable CEUS to quantify organ perfusion. We performed an exploratory study to determine the ability of CEUS to detect changes in renal perfusion and to correlate them with effective renal plasma flow. METHODS: CEUS with destruction-refilling sequences was studied in 10 healthy subjects, at baseline and during infusion of angiotensin II (AngII) at low (1 ng/kg/min) and high dose (3 ng/kg/min) and 1 h after oral captopril (50 mg). Perfusion index (PI) was obtained and compared with the effective renal plasma flow (ERPF) obtained by parallel para-aminohippurate (PAH) clearance. RESULTS: Median PI decreased from 188.6 (baseline) to 100.4 with low-dose AngII (-47%; P < 0.02) and to 66.1 with high-dose AngII (-65%; P < 0.01) but increased to 254.7 with captopril (+35%; P > 0.2). These changes parallelled those observed with ERPF, which changed from a median of 672.1 mL/min (baseline) to 572.3 (low-dose AngII, -15%, P < 0.05) and to 427.2 (high-dose AngII, -36%, P < 0.001) and finally 697.1 (captopril, +4%, P < 0.02). CONCLUSIONS: This study demonstrates that CEUS is able to detect changes in human renal cortical microcirculation as induced by AngII infusion and/or captopril administration. The changes in perfusion indices parallel those in ERPF as obtained by PAH clearance.

Motion compensation strategies in magnetic resonance imaging.

Image quality in magnetic resonance imaging (MRI) is considerably affected by motion. Therefore, motion is one of the most common sources of artifacts in contemporary cardiovascular MRI. Such artifacts in turn may easily lead to misinterpretations in the images and a subsequent loss in diagnostic quality. Hence, there is considerable research interest in strategies that help to overcome these limitations at minimal cost in time, spatial resolution, temporal resolution, and signal-to-noise ratio. This review summarizes and discusses the three principal sources of motion: the beating heart, the breathing lungs, and bulk patient movement. This is followed by a comprehensive overview of commonly used compensation strategies for these different types of motion. Finally, a summary and an outlook are provided.

Coronary MR imaging: lumen and wall.

Coronary MR imaging is a promising noninvasive technique for the combined assessment of coronary artery anatomy and function. Anomalous coronary arteries and aneurysms can reliably be assessed in clinical practice using coronary MR imaging and the presence of significant left main or proximal multivessel coronary artery disease detected. Technical challenges that need to be addressed are further improvements in motion suppression and abbreviated scanning times aimed at improving spatial resolution and patient comfort. The development of new and specific contrast agents, high-field MR imaging with improved spatial resolution, and continued progress in MR imaging methods development will undoubtedly lead to further progress toward the noninvasive and comprehensive assessment of coronary atherosclerotic disease.