Co-operative surveillance cameras for high quality face acquisition in a real-time door monitoring system

A poster session on co-operative surveillance cameras for high quality face acquisition in a real-time door monitoring syste

Description et classification des masses mammaires pour le diagnostic du cancer du sein

Le diagnostic assisté par ordinateur du cancer du sein devient de plus en plus une nécessité vu la croissance exponentielle du nombre de mammographies effectuées chaque année. En particulier, le diagnostic des masses mammaires et leur classification suscitent actuellement un grand intérêt. En effet, la complexité des formes traitées et la difficulté rencontrée afin de les discerner nécessitent l'usage de descripteurs appropriés. Dans ce travail, des méthodes de caractérisation adaptées aux pathologies mammaires sont proposées ainsi que l'étude de différentes méthodes de classification est abordée. Afin de pouvoir analyser les formes des masses, une étude concernant les différentes techniques de segmentation est réalisée. Cette étude nous a permis de nous orienter vers le modèle du level set basé sur la minimisation de l'énergie de la région évolutive. Une fois les images sont segmentées, une étude des différents descripteurs proposés dans la littérature est menée. Cependant, ces propositions présentent certaines limites telles que la sensibilité au bruit, la non invariance aux transformations géométriques et la description générale et imprécise des lésions. Dans ce contexte, nous proposons un nouveau descripteur intitulé les points terminaux du squelette (SEP) afin de caractériser les spiculations du contour des masses tout en respectant l'invariance à l'échelle. Un deuxième descripteur nommé la sélection des protubérances (PS) est proposé. Il assure de même le critère d'invariance et la description précise de la rugosité du contour. Toutefois, le SEP et le PS sont sensibles au bruit. Une troisième proposition intitulée le descripteur des masses spiculées (SMD) assurant une bonne robustesse au bruit est alors réalisée. Dans l'objectif de comparer différents descripteurs, une étude comparative entre différents classifieurs est effectuée. Les séparateurs à vaste marge (SVM) fournissent pour tous les descripteurs considérés le meilleur résultat de classification. Finalement, les descripteurs proposés ainsi que d'autres couramment utilisés dans le domaine du cancer du sein sont comparés afin de tester leur capacité à caractériser convenablement le contour des masses en question. La performance des trois descripteurs proposés et notamment le SMD est mise en évidence à travers les comparaisons effectuées.The computer-aided diagnosis of breast cancer is becoming increasingly a necessity given the exponential growth of performed mammograms. In particular, the breast mass diagnosis and classification arouse nowadays a great interest. Indeed, the complexity of processed forms and the difficulty to distinguish between them require the use of appropriate descriptors. In this work, characterization methods suitable for breast pathologies are proposed and the study of different classification methods is addressed. In order to analyze the mass shapes, a study about the different segmentation techniques in the context of breast mass detection is achieved. This study allows to adopt the level set model based on minimization of region-scalable fitting energy. Once the images are segmented, a study of various descriptors proposed inthe literature is conducted. Nevertheless, these proposals have some limitations such as sensitivity to noise, non invariance to geometric transformations and imprecise and general description of lesions. In this context, we propose a novel descriptor entitled the Skeleton End Points descriptor (SEP) in order to better characterize spiculations in mass contour while respecting the scale invariance. A second descriptor named the Protuberance Selection (PS) is proposed. It ensures also the same invariance criterion and the accurate description of the contour roughness. However, SEP and PS proposals are sensitive to noise. A third proposal entitled Spiculated Mass Descriptor (SMD) which has good robustness to noise is then carried out. In order to compare different descriptors, a comparative study between different classifiers is performed. The Support Vector Machine (SVM) provides for all considered descriptors the best classification result. Finally, the proposed descriptors and others commonly used in the breast cancer field are compared to test their ability to characterize the considered mass contours.EVRY-Bib. électronique (912289901) / SudocSudocFranceF

Breast Cancer : automatic detection and risk analysis through machine learning algorithms, using mammograms

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), 2021, Universidade de Lisboa, Faculdade de CiênciasCom 2.3 milhões de casos diagnosticados em todo o Mundo, durante o ano de 2020, o cancro da mama tornou-se aquele com maior incidência, nesse mesmo ano, considerando ambos os sexos. Anualmente, em Portugal, são diagnosticados aproximadamente sete mil (7000) novos casos de cancro da mama, com mil oitocentas (1800) mulheres a morrerem, todos os anos, devido a esta doença - indicando uma taxa de mortalidade de aproximadamente 5 mulheres por dia. A maior parte dos diagnósticos de cancro da mama ocorrem ao nível de programas de rastreio, que utilizam mamografia. Esta técnica de imagem apresenta alguns problemas: o facto de ser uma imagem a duas dimensões leva a que haja sobreposição de tecidos, o que pode mascarar a presença de tumores; e a fraca sensibilidade a mamas mais densas, sendo estas caraterísticas de mulheres com risco de cancro da mama mais elevado. Como estes dois problemas dificultam a leitura das mamografias, grande parte deste trabalhou focou-se na verificação do desempenho de métodos computacionais na tarefa de classificar mamografias em duas classes: cancro e não-cancro. No que diz respeito à classe “não cancro” (N = 159), esta foi constituída por mamografias saudáveis (N=84), e por mamografias que continham lesões benignas (N=75). Já a classe “cancro” continha apenas mamografias com lesões malignas (N = 73). A discriminação entre estas duas classes foi feita com recurso a algoritmos de aprendizagem automática. Múltiplos classificadores foram otimizados e treinados (Ntreino=162, Nteste = 70), recorrendo a um conjunto de características previamente selecionado, que descreve a textura de toda a mamografia, em vez de apenas uma única Região de Interesse. Estas características de textura baseiam-se na procura de padrões: sequências de pixéis com a mesma intensidade, ou pares específicos de pixéis. O classificador que apresentou uma performance mais elevada foi um dos Support Vector Machine (SVM) treinados – AUC= 0.875, o que indica um desempenho entre o bom e o excelente. A Percent Mammographic Density (%PD) é um importante fator de risco no que diz respeito ao desenvolvimento da doença, pelo que foi estudado se a sua adição ao set de features selecionado resultaria numa melhor performance dos classificadores. O classificador, treinado e otimizado utilizando as features de textura e os cálculos de %PD, com maior capacidade discriminativa foi um Linear Discriminant Analysis (LDA) – AUC = 0.875. Uma vez que a performance é igual à obtida com o classificador que utiliza apenas features de textura, conclui-se que a %PD parece não contribuir com informação relevante. Tal pode ocorrer porque as próprias características de textura já têm informação sobre a densidade da mama. De forma a estudar-se de que modo o desempenho destes métodos computacionais pode ser afetado por piores condições de aquisição de imagem, foi simulado ruído gaussiano, e adicionado ao set de imagens utilizado para testagem. Este ruído, adicionado a cada imagem com quatro magnitudes diferentes, resultou numa AUC de 0.765 para o valor mais baixo de ruído, e numa AUC de 0.5 para o valor de ruído mais elevado. Tais resultados indicam que, para níveis de ruído mais baixo, o classificador consegue, ainda assim, manter uma performance satisfatória – o que deixa de se verificar para valores mais elevados de ruído. Estudou-se, também, se a aplicação de técnicas de filtragem – com um filtro mediana – poderia ajudar a recuperar informação perdida aquando da adição de ruído. A aplicação do filtro a todas as imagens ruidosas resultou numa AUC de 0.754 para o valor mais elevado de ruído, atingindo assim um desempenho similar ao set de imagens menos ruidosas, antes do processo de filtragem (AUC=0.765). Este resultados parecem indicar que, na presença de más condições de aquisição, a aplicação de um filtro mediana pode ajudar a recuperar informação, conduzindo assim a um melhor desempenho dos métodos computacionais. No entanto, esta mesma conclusão parece não se verificar para valores de ruído mais baixo onde a AUC após filtragem acaba por ser mais reduzida. Tal resultado poderá indicar que, em situações onde o nível de ruído é mais baixo, a técnica de filtragem não só remove o ruído, como acaba também por, ela própria, remover informação ao nível da textura da imagem. De modo a verificar se mamas com diferentes densidades afetavam a performance do classificador, foram criados três sets de teste diferentes, cada um deles contendo imagens de mamas com a mesma densidade (1, 2, e 3). Os resultados obtidos indicam-nos que um aumento na densidade das mamas analisadas não resulta, necessariamente, numa diminuição da capacidade em discriminar as classes definidas (AUC = 0.864, AUC = 0.927, AUC= 0.905; para as classes 1, 2, e 3 respetivamente). A utilização da imagem integral para analisar de textura, e a utilização de imagens de datasets diferentes (com dimensões de imagem diferentes), poderiam introduzir um viés na classificação, especialmente no que diz respeito às diferentes áreas da mama. Para verificar isso mesmo, utilizando o coeficiente de correlação de Pearson, ρ = 0.3, verificou-se que a área da mama (e a percentagem de ocupação) tem uma fraca correlação com a classificação dada a cada imagem. A construção do classificador, para além de servir de base a todos os testes apresentados, serviu também o propósito de criar uma interface interativa, passível de ser utilizada como ficheiro executável, sem necessidade de instalação de nenhum software. Esta aplicação permite que o utilizador carregue imagens de mamografia, exclua background desnecessário para a análise da imagem, extraia features, teste o classificador construído e dê como output, no ecrã, a classe correspondente à imagem carregada. A análise de risco de desenvolvimento da doença foi conseguida através da análise visual da variação dos valores das features de textura ao longo dos anos para um pequeno set (N=11) de mulheres. Esta mesma análise permitiu descortinar aquilo que parece ser uma tendência apresentada apenas por mulheres doentes, na mamografia imediatamente anterior ao diagnóstico da doença. Todos os resultados obtidos são descritos profundamente ao longo deste documento, onde se faz, também, uma referência pormenorizada a todos os métodos utilizados para os obter. O resultado da classificação feita apenas com as features de textura encontra-se dentro dos valores referenciados no estado-da-arte, indicando que o uso de features de textura, por si só, demonstrou ser profícuo. Para além disso, tal resultado serve também de indicação que o recurso a toda a imagem de mamografia, sem o trabalho árduo de definição de uma Região de Interesse, poderá ser utilizado com relativa segurança. Os resultados provenientes da análise do efeito da densidade e da área da mama, dão também confiança no uso do classificador. A interface interativa que resultou desta primeira fase de trabalho tem, potencialmente, um diferenciado conjunto de aplicações: no campo médico, poderá servir de auxiliar de diagnóstico ao médico; já no campo da análise computacional, poderá servir para a definição da ground truth de potenciais datasets que não tenham legendas definidas. No que diz respeito à análise de risco, a utilização de um dataset de dimensões reduzidas permitiu, ainda assim, compreender que existem tendências nas variações das features ao longo dos anos, que são especificas de mulheres que desenvolveram a doença. Os resultados obtidos servem, então, de indicação que a continuação desta linha de trabalho, procurando avaliar/predizer o risco, deverá ser seguida, com recurso não só a datasets mais completos, como também a métodos computacionais de aprendizagem automática.Two million and three hundred thousand Breast Cancer (BC) cases were diagnosed in 2020, making it the type of cancer with the highest incidence that year, considering both sexes. Breast Cancer diagnosis usually occurs during screening programs using mammography, which has some downsides: the masking effect due to its 2-D nature, and its poor sensitivity concerning dense breasts. Since these issues result in difficulties reading mammograms, the main part of this work aimed to verify how a computer vision method would perform in classifying mammograms into two classes: cancer and non-cancer. The ‘non-cancer group’ (N=159) was composed by images with healthy tissue (N=84) and images with benign lesions (N=75), while the cancer group (N=73) contained malignant lesions. To achieve this, multiple classifiers were optimized and trained (Ntrain = 162, Ntest = 70) with a previously selected ideal sub-set of features that describe the texture of the entire image, instead of just one small Region of Interest (ROI). The classifier with the best performance was Support Vector Machine (SVM), (AUC = 0.875), which indicates a good-to-excellent capability discriminating the two defined groups. To assess if Percent Mammographic Density (%PD), an important risk factor, added important information, a new classifier was optimized and trained using the selected sub-set of texture features plus the %PD calculation. The classifier with the best performance was a Linear Discriminant Analysis (LDA), (AUC=0.875), which seems to indicate, once it achieves the same performance as the classifier using only texture features, that there is no relevant information added from %PD calculations. This happens because texture already includes information on breast density. To understand how the classifier would perform in worst image acquisition conditions, gaussian noise was added to the test images (N=70), with four different magnitudes (AUC= 0.765 for the lowest noise value vs. AUC ≈ 0.5 for the highest). A median filter was applied to the noised images towards evaluating if information could be recovered. For the highest noise value, after filtering, the AUC was very close to the one obtained for the lowest noise value before filtering (0.754 vs 0.765), which indicates information recovery. The effect of density in classifier performance was evaluated by constructing three different test sets, each containing images from a density class (1,2,3). It was seen that an increase in density did not necessarily resulted in a decrease in performance, which indicates that the classifier is robust to density variation (AUC = 0.864, AUC= 0.927, AUC= 0.905 ; for class 1, 2, and 3 respectively). Since the entire image is being analyzed, and images come from different datasets, it was verified if breast area was adding bias to classification. Pearson correlation coefficient provided an output of ρ = 0.22, showing that there is a weak correlation between these two variables. Finally, breast cancer risk was assessed by visual texture feature analysis through the years, for a small set of women (N=11). This visual analysis allowed to unveil what seems to be a pattern amongst women who developed the disease, in the mammogram immediately before diagnosis. The details of each phase, as well as the associated final results are deeply described throughout this document. The work done in the first classification task resulted in a state-of-the-art performance, which may serve as foundation for new research in the area, without the laborious work of ROI definition. Besides that, the use of texture features alone proved to be fruitful. Results concerning risk may serve as basis for future work in the area, with larger datasets and the incorporation of Computer Vision methods

What is the intimate experience of couples following the woman\u27s cancer-related breast surgery?

The purpose of this phenomenological study is to describe and interpret the experience of couples following the woman\u27s surgery for breast cancer, in order to gain an in depth understanding of how this mutilating surgery affects their everyday lives. Breast cancer is the most prevalent form of cancer in Australian women, and in most industrialised nations. Women in Australia have a one in fourteen lifetime risk of developing the disease. Little is known concerning what meanings this surgery brings to the intimacy experienced between partners. It is important to seek knowledge of the phenomenon of intimacy, how the couples understand it, and how breast surgery has affected it, if at all. Literature to date has focused on the psychological and physiological effects of mastectomy on women, with little attention given to the plight of partners. A phenomenological approach was chosen for the study, and a purposive sample of seven couples was selected for the interviews. Primary data was obtained from audio taped interviews and from participant observation. Data analysis followed the protocol outlined by Colaizzi (1978), and seeks to describe, interpret and extrapolate common themes and meanings from the data

Characterization and modelling of complex motion patterns

Movement analysis is the principle of any interaction with the world and the survival of living beings completely depends on the effciency of such analysis. Visual systems have remarkably developed eficient mechanisms that analyze motion at different levels, allowing to recognize objects in dynamical and cluttered environments. In artificial vision, there exist a wide spectrum of applications for which the study of complex movements is crucial to recover salient information. Yet each domain may be different in terms of scenarios, complexity and relationships, a common denominator is that all of them require a dynamic understanding that captures the relevant information. Overall, current strategies are highly dependent on the appearance characterization and usually they are restricted to controlled scenarios. This thesis proposes a computational framework that is inspired in known motion perception mechanisms and structured as a set of modules. Each module is in due turn composed of a set of computational strategies that provide qualitative and quantitative descriptions of the dynamic associated to a particular movement. Diverse applications were herein considered and an extensive validation was performed for each of them. Each of the proposed strategies has shown to be reliable at capturing the dynamic patterns of different tasks, identifying, recognizing, tracking and even segmenting objects in sequences of video.Resumen. El análisis del movimiento es el principio de cualquier interacción con el mundo y la supervivencia de los seres vivos depende completamente de la eficiencia de este tipo de análisis. Los sistemas visuales notablemente han desarrollado mecanismos eficientes que analizan el movimiento en diferentes niveles, lo cual permite reconocer objetos en entornos dinámicos y saturados. En visión artificial existe un amplio espectro de aplicaciones para las cuales el estudio de los movimientos complejos es crucial para recuperar información saliente. A pesar de que cada dominio puede ser diferente en términos de los escenarios, la complejidad y las relaciones de los objetos en movimiento, un común denominador es que todos ellos requieren una comprensión dinámica para capturar información relevante. En general, las estrategias actuales son altamente dependientes de la caracterización de la apariencia y por lo general están restringidos a escenarios controlados. Esta tesis propone un marco computacional que se inspira en los mecanismos de percepción de movimiento conocidas y esta estructurado como un conjunto de módulos. Cada módulo esta a su vez compuesto por un conjunto de estrategias computacionales que proporcionan descripciones cualitativas y cuantitativas de la dinámica asociada a un movimiento particular. Diversas aplicaciones fueron consideradas en este trabajo y una extensa validación se llevó a cabo para cada uno de ellas. Cada una de las estrategias propuestas ha demostrado ser fiable en la captura de los patrones dinámicos de diferentes tareas identificando, reconociendo, siguiendo e incluso segmentando objetos en secuencias de video.Doctorad

Analyse de la relation entre les déformations scoliotiques du tronc et celles des structures osseuses sous-jacentes

RÉSUMÉ La scoliose idiopathique adolescente est une déformation tridimensionnelle complexe de la colonne vertébrale et de la cage thoracique qui entraine des déformations visibles à la surface du tronc. On remarque généralement une asymétrie des épaules, des omoplates, de la taille et du bassin ainsi qu’une bosse dans le dos. Ces déformations esthétiques constituent, d’une part, les premiers signes d’une scoliose, et d’autre part, la principale préoccupation des jeunes patients qui voient leur corps se développer différemment des jeunes de leur âge. Les outils cliniques utilisés pour quantifier les déformations du tronc, comme le scoliomètre ou le fil à plomb, sont peu fiables. C’est pourquoi, aujourd’hui, l’évaluation de la scoliose repose principalement sur des radiographies de face et de profil du tronc complet. Celles-ci permettent d’apprécier le type de courbure rachidienne et de quantifier son degré de sévérité, en fonction de quoi une stratégie de traitement sera décidée. Cependant, une exposition répétée des patients aux rayons X peut entrainer des effets indésirables sur leur santé. De plus, ces paramètres radiographiques ne permettent pas de documenter les déformations esthétiques. Cette différence notable entre ce que le patient perçoit, et ce que le clinicien est capable d’évaluer, peut mener à l’insatisfaction des patients suite au traitement. Comparativement aux radiographies, la surface du tronc reconstruite par les systèmes de numériseurs optiques 3D représente mieux les déformations que les patients observent et dont ils se soucient principalement, comme la gibbosité. De plus, l’absence de rayonnement ionisant est un avantage majeur de ces systèmes optiques, qui favorise une évaluation aussi fréquente que souhaité. Toutefois, l’absence de consensus sur un ensemble de mesures des déformations de la surface du tronc fait en sorte qu’elles restent encore considérées comme secondaires dans l’évaluation clinique; pourtant elles sont au coeur des préoccupations des patients. De cette double problématique, découle la question de recherche globale de cette thèse : comment compléter, voire remplacer, les évaluations clinique et radiographique actuelles de la scoliose par de l’information quantitative obtenue de manière non irradiante et qui permet de prendre davantage en considération les préoccupations des patients par rapport à leurs déformations esthétiques du tronc ? Parmi les premiers signes de scoliose, la gibbosité est une déformation esthétique qui ne peut être évaluée sur des radiographies, ni sur une reconstruction 3D de la colonne vertébrale.----------ABSTRACT Adolescent idiopathic scoliosis is a complex three-dimensional deformation of the spine and rib cage which leads to visible deformations at the trunk surface. The first signs of scoliosis include a hump on the back, a lateral shift of the trunk and asymmetries of the shoulders, the scapula, the waist and the hips. These esthetic deformities constitute major concern of patients and the reason for which they seek treatment. Currently, the tools available in clinical practice to quantify trunk deformations have limited reliability. For this reason, current scoliosis assessment is mainly based on frontal and lateral radiographs of the entire spine. These images allow clinicians to determine the type of the spinal curvature and its severity, according to which the treatment strategy is decided. However, the repeated exposure of patients to X-ray radiation can be harmful. Moreover, these radiographic measures do not give an indication as to the esthetic deformities of the trunk. This significant difference between what patients perceive and what clinicians are able to evaluate can lead to patient dissatisfaction following treatment. Compared to X-rays, the trunk surface acquired and reconstructed in 3D using optical digitizers better represents the deformations that patients observe and are primarily concerned with, such as the rib hump. In addition, the major advantage of these optical systems is their lack of ionizing radiation, thus allowing for a more frequent scoliosis assessment when compared to X-rays. However, there is currently no consensus on a set of indices that optimally quantifies trunk surface deformations. For this reason, trunk surface indices are still considered as secondary in the clinical evaluation, even though they are at the heart of the patients’ preoccupations. These problems lead to the main research question of this thesis: How can we complete, or even replace, the current clinical and radiographic evaluations of scoliosis with quantitative information obtained without ionizing radiation that takes more into account the patients’ concerns about their cosmetic trunk deformities? Among the first signs of scoliosis, the rib hump is a cosmetic deformity that cannot be assessed on radiographs, nor on a 3D reconstruction of the spine. It is mainly associated with rib cage deformity. It is therefore intuitive to suppose that the axial rotations of the ribs and of the back surface are highly correlated. Nevertheless, previous works have failed to demonstrate a strong relationship between these measurements. This might be explained by the limited accuracy of the technique used for the 3D reconstruction of the ribs. Consequently, in this work, a novel metho

Bryophyte Ecology

Bryophyte Ecology is an ebook comprised of 5 volumes written by Janice Glime, Professor Emerita of Biological Sciences at Michigan Technological University. Chapter coauthors include Irene Bisang, S. Robbert Gradstein, J. Lissner, W. J. Boelema, and D. H. Wagner. To download smaller sections of Bryophyte Ecology, visit: https://digitalcommons.mtu.edu/bryophyte-ecology/https://digitalcommons.mtu.edu/oabooks/1003/thumbnail.jp

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp