No / Objectives The presence of microcalcifications (MCs), clusters of tiny calcium deposits that appear as small bright spots in a mammogram, has been considered as a very important indicator for breast cancer diagnosis. Much research has been performed for developing computer-aided systems for the accurate identification of MCs, however, the computer-based automatic detection of MCs has been shown difficult because of the complicated nature of surrounding of breast tissue, the variation of MCs in shape, orientation, brightness and size. Methods and materials This paper presents a new approach for the effective detection of MCs by incorporating a knowledge-discovery mechanism in the genetic algorithm (GA). In the proposed approach, called knowledge-discovery incorporated genetic algorithm (KD-GA), the genetic algorithm is used to search for the bright spots in mammogram and a knowledge-discovery mechanism is integrated to improve the performance of the GA. The function of the knowledge-discovery mechanism includes evaluating the possibility of a bright spot being a true MC, and adaptively adjusting the associated fitness values. The adjustment of fitness is to indirectly guide the GA to extract the true MCs and eliminate the false MCs (FMCs) accordingly. Results and conclusions The experimental results demonstrate that the incorporation of knowledge-discovery mechanism into the genetic algorithm is able to eliminate the FMCs and produce improved performance comparing with the conventional GA methods. Furthermore, the experimental results show that the proposed KD-GA method provides a promising and generic approach for the development of computer-aided diagnosis for breast cancer.

Le manuscrit suivant porte sur le développement d’une méthodologie de cartographie de la susceptibilité magnétique. Cette méthodologie a été appliquée au niveau des seins à des fins de détection de microcalcifications. Afin de valider ces algorithmes, un fantôme numérique ainsi qu’un fantôme réel ont été créés. À l’aide de ces images, les paramètres modifiables de notre méthodologie ont été ajustés. Par la suite, les problèmes reliés à l’imagerie du sein ont été explorés, tel la présence de gras ainsi que la proximité des poumons. Finalement, des images in vivo, acquises à 1.5 et 7.0 Tesla ont été analysées par notre méthodologie. Sur ces images 1.5T, nous avons réussi à observer la présence de microcalcifications. D’un autre côté, les images 7.0T nous ont permis de présenter un meilleur contraste que les images standards de magnitude. / The following manuscript is about the development of a methodology called quantitative susceptibility mapping. This methodology was applied to the breast with the purpose of detecting microcalcifications. To validate these algorithms, a digital phantom and a water phantom were created. Using these images, adjustable parameters were adjusted on our methodology. Thereafter, problems related to breast imaging, like the presence of fat and the proximity of the lungs, were explored. Finally, in vivo images, acquired at 1.5 and 7.0 Tesla were analyzed by our methodology. On these 1.5T images, we successfully observed the presence of microcalcifications. On the other hand, the 7.0T images allowed us to provide a better contrast than the standard magnitude images.

Le manuscrit suivant porte sur le développement d’une méthodologie de cartographie de la susceptibilité magnétique. Cette méthodologie a été appliquée au niveau des seins à des fins de détection de microcalcifications. Afin de valider ces algorithmes, un fantôme numérique ainsi qu’un fantôme réel ont été créés. À l’aide de ces images, les paramètres modifiables de notre méthodologie ont été ajustés. Par la suite, les problèmes reliés à l’imagerie du sein ont été explorés, tel la présence de gras ainsi que la proximité des poumons. Finalement, des images in vivo, acquises à 1.5 et 7.0 Tesla ont été analysées par notre méthodologie. Sur ces images 1.5T, nous avons réussi à observer la présence de microcalcifications. D’un autre côté, les images 7.0T nous ont permis de présenter un meilleur contraste que les images standards de magnitude. / The following manuscript is about the development of a methodology called quantitative susceptibility mapping. This methodology was applied to the breast with the purpose of detecting microcalcifications. To validate these algorithms, a digital phantom and a water phantom were created. Using these images, adjustable parameters were adjusted on our methodology. Thereafter, problems related to breast imaging, like the presence of fat and the proximity of the lungs, were explored. Finally, in vivo images, acquired at 1.5 and 7.0 Tesla were analyzed by our methodology. On these 1.5T images, we successfully observed the presence of microcalcifications. On the other hand, the 7.0T images allowed us to provide a better contrast than the standard magnitude images.

Τα υπολογιστικά συστήματα υποβοήθησης ανίχνευσης και διάγνωσης αλλοιώσεων του μαστού έχουν προταθεί στις διάφορες απεικονιστικές τεχνικές «ως δεύτεροι αναγνώστες» με σκοπό να αυξήσουν τη διαγνωστική ακρίβεια του ακτινολόγου και να μειώσουν τη μεταβλητότητα μεταξύ και ενδο-παρατηρητή κατά την ερμηνεία της μαστογραφικής εικόνας. Η αυτόματη διάγνωση των ομάδων μικροαποτιτανώσεων αποτελεί ανοικτό ερευνητικό ζήτημα. Τα προταθέντα συστήματα ψηφιακής υποβοήθησης διάγνωσης ομάδων μικροαποτιτανώσεων ακολουθούν δύο βασικές προσεγγίσεις: (α) ανάλυση μορφολογίας των μεμονωμένων μικροαποτιτανώσεων της ομάδας και (β) ανάλυση υφής των περιοχών ενδιαφέροντος της μαστογραφικής εικόνας που περικλείει την ομάδα μικροαποτιτανώσεων. Τα συστήματα αυτά διατυπώνουν μαθηματικά το κλινικό ερώτημα αξιοποιώντας μεθόδους επεξεργασίας και ανάλυσης εικόνας με σκοπό την ποσοτικοποίηση δομικών και λειτουργικών παραμέτρων του απεικονιζόμενου ιστού. Στην παρούσα εργασία περιγράφονται οι τρέχουσες προσεγγίσεις στην μεθοδολογία ανάπτυξης συστημάτων υποβοήθησης διάγνωσης ομάδων μικροαποτιτανώσεων στην μαστογραφία ακτίνων-Χ. Σκοπός είναι η ανάδειξη των πλεονεκτημάτων, μειονεκτημάτων και προκλήσεων των διαφορετικών προσεγγίσεων της ψηφιακής υποβοήθησης διάγνωσης, η καταγραφή των εξελίξεων και αναδυόμενων μεθόδων καθώς και η διερεύνηση και αποτύπωση των μελλοντικών ερευνητικών βημάτων. / Computer-aided detection and diagnosis schemes have been proposed across breast imaging modalities to improve diagnostic accuracy and reduce inter- and intra-observer variability in image interpretation. Computer-aided diagnosis schemes for microcalcification clusters in mammography are based on morphology analysis of individual microcalcifications and on texture analysis of the depicted breast tissue. The current study reviews major approaches in the development of computer-aided diagnosis schemes for microcalcification clusters in mammography, while recent advances and challenges of each methodological approach are highlighted.

O câncer de mama é hoje uma das principais causas de mortalidade de mulheres em todo o mundo. Porém, a sua detecção no estágio inicial de desenvolvimento aumenta consideravelmente as chances de cura. Exatamente por isso estão sendo desenvolvidos vários tipos de sistemas computacionais baseados em processamento de imagens em centros de pesquisas no mundo todo, a fim de auxiliar o radiologista na precisão do seu diagnóstico. A pesquisa aqui apresentada se insere nesse contexto e consistiu no desenvolvimento de um sistema computacional para detectar uma das estruturas que podem ser indício da presença do câncer de mama: os agrupamentos (\"clusters\") de microcalcificações. O sistema aqui apresentado tem como fonte de dados mamogramas digitalizados, nos quais são aplicadas técnicas de processamento para extrair as regiões de interesse e detectar os possíveis \"clusters\" existentes. Os resultados dos testes realizados mostraram que o sistema desenvolvido apresentou uma eficiência de 94% na identificação correta de \"clusters\". / Breast cancer is one of the main causes of women death all over the world. However, early detection of the disease increases greatly the possibility of cure. Therefore, several types of computer systems based on image processing are being developed by many research groups in order to aid the radiologist in the accuracy of the diagnosis. The work presented here is inserted in this context corresponding to the development of a computer system designed to detect microcalcifications clusters - structures which can be a strong indicative of breast cancer. This system database is digitized mammograms, to which image processing techniques are applied in order to detect regions of interest and the possible clusters. The results from the tests have shown an efficacy of 94% of the system in clusters correct identification.

O câncer de mama é hoje uma das principais causas de mortalidade de mulheres em todo o mundo. Porém, a sua detecção no estágio inicial de desenvolvimento aumenta consideravelmente as chances de cura. Exatamente por isso estão sendo desenvolvidos vários tipos de sistemas computacionais baseados em processamento de imagens em centros de pesquisas no mundo todo, a fim de auxiliar o radiologista na precisão do seu diagnóstico. A pesquisa aqui apresentada se insere nesse contexto e consistiu no desenvolvimento de um sistema computacional para detectar uma das estruturas que podem ser indício da presença do câncer de mama: os agrupamentos (\"clusters\") de microcalcificações. O sistema aqui apresentado tem como fonte de dados mamogramas digitalizados, nos quais são aplicadas técnicas de processamento para extrair as regiões de interesse e detectar os possíveis \"clusters\" existentes. Os resultados dos testes realizados mostraram que o sistema desenvolvido apresentou uma eficiência de 94% na identificação correta de \"clusters\". / Breast cancer is one of the main causes of women death all over the world. However, early detection of the disease increases greatly the possibility of cure. Therefore, several types of computer systems based on image processing are being developed by many research groups in order to aid the radiologist in the accuracy of the diagnosis. The work presented here is inserted in this context corresponding to the development of a computer system designed to detect microcalcifications clusters - structures which can be a strong indicative of breast cancer. This system database is digitized mammograms, to which image processing techniques are applied in order to detect regions of interest and the possible clusters. The results from the tests have shown an efficacy of 94% of the system in clusters correct identification.

The field of medicine is regularly faced with the challenge of utilizing information that is complicated or difficult to characterize. Physicians often must use their best judgment in reaching decisions or recommendations for treatment in the clinical setting. The goal of this thesis is to use innovative statistical tools in tackling three specific challenges of this nature from current healthcare applications. The first aim focuses on developing a novel approach to meta-analysis when combining binary data from multiple studies of paired design, particularly in cases of high heterogeneity between studies. The challenge is in properly accounting for heterogeneity when dealing with a low or moderate number of studies, and with a rarely occurring outcome. The proposed approach uses a Rasch model for translating data from multiple paired studies into a unified structure that allows for properly handling variability associated with both pair effects and study effects. Analysis is then performed using a Bayesian hierarchical structure, which accounts for heterogeneity in a direct way within the variances of the separate generating distributions for each model parameter. This approach is applied to the debated topic within the dental community of the comparative effectiveness of materials used for pit-and-fissure sealants. The second and third aims of this research both have applications in early detection of breast cancer. The interpretation of a mammogram is often difficult since signs of early disease are often minuscule, and the appearance of even normal tissue can be highly variable and complex. Physicians often have to consider many important pieces of the whole picture when trying to assess next steps. The final two aims focus on improving the interpretation of findings in mammograms to aid in early cancer detection. When dealing with high frequency and irregular data, as is seen in most medical images, the behaviors of these complex structures are often difficult or impossible to quantify by standard modeling techniques. But a commonly occurring phenomenon in high-frequency data is that of regular scaling. The second aim in this thesis is to develop and evaluate a wavelet-based scaling estimator that reduces the information in a mammogram down to an informative and low-dimensional quantification of the innate scaling behavior, optimized for use in classifying the tissue as cancerous or non-cancerous. The specific demands for this estimator are that it be robust with respect to distributional assumptions on the data, and with respect to outlier levels in the frequency domain representation of the data. The final aim in this research focuses on enhancing the visualization of microcalcifications that are too small to capture well on screening mammograms. Using scale-mixing discrete wavelet transform methods, the existing detail information contained in a very small and course image will be used to impute scaled details at finer levels. These "informed" finer details will then be used to produce an image of much higher resolution than the original, improving the visualization of the object. The goal is to also produce a confidence area for the true location of the shape's borders, allowing for more accurate feature assessment. Through the more accurate assessment of these very small shapes, physicians may be more confident in deciding next steps.