This dissertation presents a contribution to quantitative microwave imaging for breast tumor detection. The study made in the frame of a joint supervision Ph.D. thesis between University Paris-SUD 11 (France) and Mälardalen University (Sweden), has been conducted through two experimental microwave imaging setups, the existing 2.45 GHz planar camera (France) and the multi-frequency flexible robotic system, (Sweden), under development. In this context a 2D scalar flexible numerical tool based on a Newton-Kantorovich (NK) scheme, has been developed. Quantitative microwave imaging is a three dimensional vectorial nonlinear inverse scattering problem, where the complex permittivity of an object is reconstructed from the measured scattered field, produced by the object. The NK scheme is used in order to deal with the nonlinearity and the ill-posed nature of this problem. A TM polarization and a two dimensional medium configuration have been considered in order to avoid its vectorial aspect. The solution is found iteratively by minimizing the square norm of the error with respect to the scattered field data. Consequently, the convergence of such iterative process requires, at least two conditions. First, an efficient calibration of the experimental system has to be associated to the minimization of model errors. Second, the mean square difference of the scattered field introduced by the presence of the tumor has to be large enough, according to the sensitivity of the imaging system. The existing planar camera associated to a flexible 2D scalar NK code, are considered as an experimental platform for quantitative breast imaging. A preliminary numerical study shows that the multi-view planar system is quite efficient for realistic breast tumor phantoms, according to its characteristics (frequency, planar geometry and water as a coupling medium), as long as realistic noisy data are considered. Furthermore, a multi-incidence planar system, more appropriate in term of antenna-array arrangement, is proposed and its concept is numerically validated. On the other hand, an experimental work which includes a new fluid-mixture for the realization of a narrow band cylindrical breast phantom, a deep investigation in the calibration process and model error minimization, is presented. This conducts to the first quantitative reconstruction of a realistic breast phantom by using multi-view data from the planar camera. Next, both the qualitative and quantitative reconstruction of 3D inclusions into the cylindrical breast phantom, by using data from all the retina, are shown and discussed. Finally, the extended work towards the flexible robotic system is presented. / A dissertation prepared through an international convention for a joint supervision thesis with Université Paris-SUD 11, France / Microwaves in biomedicine

This thesis is focused on extracting meaningful statistics for the characterization and classification of biological, medical, and financial data and contains four chapters. The first chapter contains theoretical background on scaling and wavelets, which supports the work in chapters two and three. In the second chapter, we outline a methodology for representing sequences of DNA nucleotides as numeric matrices in order to analytically investigate important structural characteristics of DNA. This methodology involves assigning unit vectors to nucleotides, placing the vectors into columns of a matrix, and accumulating across the rows of this matrix. Transcribing the DNA in this way allows us to compute the 2-D wavelet transformation and assess regularity characteristics of the sequence via the slope of the wavelet spectra. In addition to computing a global slope measure for a sequence, we can apply our methodology for overlapping sections of nucleotides to obtain an evolutionary slope. In the third chapter, we describe various ways wavelet-based scaling may be used for cancer diagnostics. There were nearly half of a million new cases of ovarian, breast, and lung cancer in the United States last year. Breast and lung cancer have highest prevalence, while ovarian cancer has the lowest survival rate of the three. Early detection is critical for all of these diseases, but substantial obstacles to early detection exist in each case. In this work, we use wavelet-based scaling on metabolic data and radiography images in order to produce meaningful features to be used in classifying cases and controls. Computer-aided detection (CAD) algorithms for detecting lung and breast cancer often focus on select features in an image and make a priori assumptions about the nature of a nodule or a mass. In contrast, our approach to analyzing breast and lung images captures information contained in the background tissue of images as well as information about specific features and makes no such a priori assumptions. In the fourth chapter, we investigate the value of social media data in building commercial default and activity credit models. We use random forest modeling, which has been shown in many instances to achieve better predictive accuracy than logistic regression in modeling credit data. This result is of interest, as some entities are beginning to build credit scores based on this type of publicly available online data alone. Our work has shown that the addition of social media data does not provide any improvement in model accuracy over the bureau only models. However, the social media data on its own does have some limited predictive power.

Current positron emission mammography (PEM) depth of interaction (DOI) enabling detectors have low scintillator to photodetector encoding ratios, RE causing high system complexity and cost. The modularized dual-ended readout block (DERB) detector combines the Anger logic block detector with dual-ended readout to increase RE while measuring DOI. To investigate the trade-off between RE and spatial resolution, scalable DERB detectors with varying RE and light guide thickness were modelled with Monte- Carlo. Simulation showed RE can increase up to six-fold compared to the dual-ended readout design without significantly degrading spatial resolution. Experimental characterization of a RE = 9 : 8 DERB detector was found to achieve super-resolution <0.5 mm for resolving crystal indices, DOI resolution of ~5 mm FWHM, and mean energy resolution of 20% without recovering photons lost to neighbouring detector modules. The model was validated by agreement of simulation results adjusted for detector quantum efficiency with experimental results.

Current positron emission mammography (PEM) depth of interaction (DOI) enabling detectors have low scintillator to photodetector encoding ratios, RE causing high system complexity and cost. The modularized dual-ended readout block (DERB) detector combines the Anger logic block detector with dual-ended readout to increase RE while measuring DOI. To investigate the trade-off between RE and spatial resolution, scalable DERB detectors with varying RE and light guide thickness were modelled with Monte- Carlo. Simulation showed RE can increase up to six-fold compared to the dual-ended readout design without significantly degrading spatial resolution. Experimental characterization of a RE = 9 : 8 DERB detector was found to achieve super-resolution <0.5 mm for resolving crystal indices, DOI resolution of ~5 mm FWHM, and mean energy resolution of 20% without recovering photons lost to neighbouring detector modules. The model was validated by agreement of simulation results adjusted for detector quantum efficiency with experimental results.

Aquesta tesi està emmarcada dins la detecció precoç de masses, un dels símptomes més clars del càncer de mama, en imatges mamogràfiques. Primerament, s'ha fet un anàlisi extensiu dels diferents mètodes de la literatura, concloent que aquests mètodes són dependents de diferent paràmetres: el tamany i la forma de la massa i la densitat de la mama. Així, l'objectiu de la tesi és analitzar, dissenyar i implementar un mètode de detecció robust i independent d'aquests tres paràmetres. Per a tal fi, s'ha construït un patró deformable de la massa a partir de l'anàlisi de masses reals i, a continuació, aquest model és buscat en les imatges seguint un esquema probabilístic, obtenint una sèrie de regions sospitoses. Fent servir l'anàlisi 2DPCA, s'ha construït un algorisme capaç de discernir aquestes regions són realment una massa o no. La densitat de la mama és un paràmetre que s'introdueix de forma natural dins l'algorisme. / This thesis deals with the detection of masses in mammographic images. As a first step, Regions of Interests (ROIs) are detected in the image using templates containing a probabilistic contour shape obtained from training over an annotated set of masses. Firstly, PCA is performed over the training set, and subsequently the template is formed as an average of the gradient of eigenmasses weighted by the top eigenvalues. The template can be deformed according to each eigenmass coefficient. The matching is formulated in a Bayesian framework, where the prior penalizes the deformation, and the likelihood requires template boundaries to agree with image edges. In the second stage, the detected ROIs are classified into being false positives or true positives using 2DPCA, where the new training set now contains ROIs with masses and ROIs with normal tissue. Mass density is incorporated into the whole process by initially classifying the two training sets according to breast density. Methods for breast density estimation are also analyzed and proposed. The results are obtained using different databases and both FROC and ROC analysis demonstrate a better performance of the approach relative to competing methods.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Images are relevant sources of information in many areas of science and technology. The processing of such information improves and optimizes its use. The image compression causes the information representation is more efficient, reducing the amount of data required to represent an image. The objective of this study is to evaluate the performance of Fractal Compression technique onto mammograms through an association between the objective criteria, provided by Peak Signal Noise Ration (PSNR); and the subjective criteria, given by visual analysis of an expert physician. Visual analysis was performed comparing mammograms compressed to different extents (compression rate) with the original image, where experts classified the compressed images as unacceptable , acceptable , good or great . In doing so, the optimal compression rate and PSNR values of mammograms was achieved, where images are considered acceptable according to experts. In order to compare the performance of fractal compression technique with another compression method, visual analysis was also done on images compressed by JPEG2000 method. / As imagens são fontes relevantes de informação em diversas áreas da ciência e tecnologia. O processamento dessas informações melhora e otimiza sua utilização. A compressão de imagens faz com que a representação da informação seja mais eficiente, reduzindo a quantidade de dados necessários para representar uma imagem. O objetivo deste trabalho é apresentar a avaliação do desempenho da compressão fractal aplicada a imagens mamográficas, pela combinação entre o critério objetivo, fornecido pela relação sinal ruído de pico (Peak Signal Noise Ratio - PSNR), e o critério subjetivo, especificado pela análise visual de médicos especialistas. A análise visual foi realizada comparando as imagens mamográficas comprimidas com diferentes taxas de compressão e a imagem original. Os especialistas classificaram as imagens comprimidas como \"inaceitável\", \"aceitável\", \"boa\" ou \"ótima\". Dessa maneira, conseguiu-se combinar a taxa de compressão e o valor de PSNR, para que as imagens comprimidas sejam consideradas aceitáveis pelos especialistas. Para avaliar o desempenho da compressão fractal foram realizados testes e análises visuais com as mesmas imagens utilizando o método de compressão JPEG2000. / Mestre em Ciências

Made available in DSpace on 2016-08-17T14:53:17Z (GMT). No. of bitstreams: 1 Ulysses Santos Sousa.pdf: 1410915 bytes, checksum: 88235f7f4a3bc07a4da1b27c23dc71ca (MD5) Previous issue date: 2011-05-13 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Cancer is one of the biggest health problems worldwide, and the breast cancer is the one that causes more deaths among women. Also it is the second most frequent type in the world. The chances of survival for a patient with breast cancer increases the sooner this disease is discovered. Several Computer Aided Detection/Diagnosis Systems has been used to assist health professionals. This work presents a methodology to discriminate and classify mammographic tissues regions in mass and non-mass. For this purpose the Shannon-Wiener‟s Diversity Index, which is applied to measure the biodiversity in ecosystem, is used to describe pattern of breast image region with four approaches: global, in circles, in rings and directional. After, a Support Vector Machine is used to classify the regions in mass and non-mass. The methodology presents promising results for classification of mammographic tissues regions in mass and non-mass, achieving 99.85% maximum accuracy. / O câncer é um dos maiores problemas de saúde mundial, sendo o câncer de mama o que mais causa óbito entre as mulheres e o segundo tipo mais freqüente no mundo. As chances de uma paciente sobreviver ao câncer de mama aumentam à medida que a doença é descoberta mais cedo. Diversos Sistemas de Detecção e Diagnóstico auxiliados por computador (Computer Aided Detection/Diagnosis) têm sido utilizados para auxiliar profissionais de saúde. Este trabalho apresenta uma metodologia de discriminação e classificação de regiões de tecidos de mamografias em massa e não massa. Para este propósito utiliza-se o Índice de Diversidade de Shannon-Wiener, comumente aplicado para medir a biodiversidade em um ecossistema, para descrever padrões de regiões de imagens de mama com quatro abordagens: global, em círculos, em anéis e direcional. Em seguida, utiliza-se o classificador Support Vector Machine para classificar estas regiões em massa e não massa. A metodologia apresenta resultados promissores para a classificação de regiões de tecidos de mamografia em massa e não massa, obtendo uma acurácia máxima de 99,85%.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-07-26T12:41:50Z No. of bitstreams: 1 melissadefatimafranca.pdf: 3155000 bytes, checksum: 747df8478e7d858eeb23d7713906b20c (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-07-27T11:28:26Z (GMT) No. of bitstreams: 1 melissadefatimafranca.pdf: 3155000 bytes, checksum: 747df8478e7d858eeb23d7713906b20c (MD5) / Made available in DSpace on 2016-07-27T11:28:26Z (GMT). No. of bitstreams: 1 melissadefatimafranca.pdf: 3155000 bytes, checksum: 747df8478e7d858eeb23d7713906b20c (MD5) Previous issue date: 2015-02-27 / O câncer constitui uma preocupação crescente da população, já que vem ganhando uma dimensão maior e convertendo-se em um evidente problema de saúde pública mundial. A Política Nacional de Controle do Câncer tem o intuito de reduzir a incidência e a mortalidade do câncer através da conscientização dos fatores de risco e medidas para a detecção precoce dos cânceres passíveis de rastreamento, com acesso a um tratamento equitativo e de qualidade. Diante da importância da realização de pesquisas nessa área o presente estudo teve como objetivos analisar a realização de exames preventivos das neoplasias do colo do útero e de mama nas funcionárias Técnico-Administrativas em Educação (TAE) de uma Universidade Pública e seus fatores associados. Para isso foi realizado um estudo epidemiológico de delineamento transversal com 399 TAEs. Como instrumento de coleta de dados utilizou-se um questionário autopreenchível com questões relacionadas à realização de exames preventivos do câncer de colo de útero e de mama e aos fatores sócio demográficos, condições de saúde e hábitos de vida. Os dados foram processados através dos programas estatísticos Statistical Package for the Social Sciences (SPSS) e Stata. Para o desfecho “não realização de Papanicolaou” as variáveis faixa etária 60-69 anos (RP 4,17 IC 95% 2,47-7,04), não ter ficado impedida de realizar atividades habituais por problemas de saúde nas duas últimas semanas (RP 2,22 IC 95% 1,04-4,75) e não estar em dia com a realização da mamografia (RP 5,00 IC 95% 2,65-9,41) aumentaram a probabilidade de não estar em dia ou não realizar o exame preventivo. Com relação à “não realização do exame clínico das mamas” as variáveis consumo adequado de verduras (RP 0,92 IC 95% 0,86-0,99), possuir um ou mais parentes com quem possa se falar sobre quase tudo (rede social) (RP 0,85 IC 95% 0,76-0,95) e possuir horário de trabalho irregular (RP 0,83 IC 95% 0,75-0,91) diminuíram a probabilidade de não realizar tal exame. O fato de não realizar ou não estar em dia com a mamografia aumentou a probabilidade de não fazer o exame clínico das mamas (RP 1,22 IC 95% 1,14-1,30). Já quanto à “não realização de mamografia” o fato de não realizar ou não estar em dia com os exames Papanicolaou (RP 3,07 IC 95% 1,86- 5,08) e exame clínico das mamas (RP 4,99 IC 95% 2,61-9,53) aumentaram a probabilidade de também não realizar o exame de mamografia. Destaca-se assim a importância da prática e incentivo de ações de prevenção do câncer na população de mulheres trabalhadoras, visando a uma melhor condição de saúde e qualidade de vida das mesmas. / Cancer is a growing concern of the population, as has been gaining increasing in size and becoming an obvious problem of global public health. The National Cancer Control Policy aims to reduce the incidence and mortality of cancer by raising awareness of risk factors and measures for the early detection of cancers amenable to screening with access to fair treatment and quality. Given the importance of conducting research in this area this study aimed to analyze the preventive examinations of cancer of the cervix and breast cancer in employees Technical and Administrative Education (TAE) of a public university and its associated factors. To this was accomplished an epidemiological cross-sectional study with 399 TAE’s. As data collection instrument used a self-administered questionnaire with questions related to preventive examinations of cervical and breast cancer and sociodemographic factors, health and lifestyle habits. Data were analyzed using the statistical software Statistical Package for Social Sciences (SPSS) and Stata. For the outcome not for Papanicolaou the variables age group 60-69 years (PR 4.17 95% CI 2.47 to 7.04), have not been prevented from performing daily activities due to health problems in the last two weeks (PR 2.22 95% CI 1.04 to 4.75) and not be up to date with mammography (PR 5.00 95% CI 2.65 to 9.41) increased the likelihood of not being up to date or not perform the screening. Regarding the non-completion of the clinical examination of the variables adequate intake of vegetables breasts (PR 0.92 95% CI from 0.86 to 0.99), have one or more relatives with whom you can talk about almost anything (social network) (PR 0.85 95% CI 0.76 to 0.95) and have irregular working hours (PR 0.83 95% CI 0.75 to 0.91) decreased the probability of not conduct such an examination. Failure to perform or not keep up with mammography increased the probability of not doing clinical breast exam (PR 1.22 95% CI 1.14 to 1.30). As for the non-completion of mammography the failure to perform or not keep up with the Papanicolaou (PR 3.07 95% CI 5.08 1, 86) and clinical breast exam (PR 4.99 95% CI 2.61 to 9.53) increased the likelihood of also not perform the examination mammography. Stands out as well the importance of practice and encouragement cancer prevention actions in the population of working women in order to better health condition and quality of life for them.

In x-ray imaging, the x-ray radiation must be the minimum necessary to achieve the required diagnostic objective, to ensure the patients safety. However, low-dose acquisitions yield images with low quality, which affect the radiologists image interpretation. Therefore, there is a compromise between image quality and radiation dose. This work proposes an image restoration framework capable of restoring low-dose acquisitions to achieve the quality of full-dose acquisitions. The contribution of the new method includes the capability of restoring images with quantum and electronic noise, pixel offset and variable detector gain. To validate the image processing chain, a simulation algorithm was proposed. The simulation generates low-dose DBT projections, starting from fulldose images. To investigate the feasibility of reducing the radiation dose in breast cancer screening programs, a simulated pre-clinical trial was conducted using the simulation and the image processing pipeline proposed in this work. Digital breast tomosynthesis (DBT) images from 72 patients were selected, and 5 human observers were invited for the experiment. The results suggested that a reduction of up to 30% in radiation dose could not be perceived by the human reader after the proposed image processing pipeline was applied. Thus, the image processing algorithm has the potential to decrease radiation levels in DBT, also decreasing the cancer induction risks associated with the exam. / Em programas de rastreamento de câncer de mama, a dose de radiação deve ser mantida o mínimo necessário para se alcançar o diagnóstico, para garantir a segurança dos pacientes. Entretanto, imagens adquiridas com dose de radiação reduzida possuem qualidade inferior. Assim, existe um equilíbrio entre a dose de radiação e a qualidade da imagem. Este trabalho propõe um algoritmo de restauração de imagens capaz de recuperar a qualidade das imagens de tomossíntese digital mamária, adquiridas com doses reduzidas de radiação, para alcançar a qualidade de imagens adquiridas com a dose de referência. As contribuições do trabalho incluem a melhoria do modelo de ruído, e a inclusão das características do detector, como o ganho variável do ruído quântico. Para a validação a cadeia de processamento, um método de simulação de redução de dose de radiação foi proposto. Para investigar a possibilidade de redução de dose de radiação utilizada na tomossíntese, um estudo pré-clínico foi conduzido utilizando o método de simulação proposto e a cadeia de processamento. Imagens clínicas de tomossíntese mamária de 72 pacientes foram selecionadas e cinco observadores foram convidados para participar do estudo. Os resultados sugeriram que, após a utilização do processamento proposto, uma redução de 30% de dose de radiação pôde ser alcançada sem que os observadores percebessem diferença nos níveis de ruído e borramento. Assim, o algoritmo de processamento tem o potencial de reduzir os níveis de radiação na tomossíntese mamária, reduzindo também os riscos de indução do câncer de mama.

Early diagnosis is a promising strategy to reduce premature mortalities and for optimal use of resources. But the absence of mathematical models specific to the data settings in LMIC’s impedes the construction of economic analysis necessary for decision-makers in the development of cancer control programs. This thesis presents a new methodology for parameterizing the natural history model of breast cancer based on data availabilities in low and middle income countries, and formulation of a control optimization problem to find the optimal screening schedule for mammography screening, solved using dynamic programming. As harms and benefits are known to increase with the increase in the number of lifetime screens, the trade-off was modeled by formulating the immediate reward as a function of false positives and life-years saved. The method presented in thesis will provide optimal screening schedules for multiple scenarios of Willingness to Pay (numeric value assigned for each life-year lived), including the resulting total number of lifetime screens per person, which can help decision-makers evaluate current resource availabilities or plan future resource needs for implementation.