Οι μικροαποτιτανώσεις είναι από τις πιο σημαντικές ενδείξεις παθήσεων του μαστού και μπορεί να αποτελέσουν πρώιμη ένδειξη καρκίνου του μαστού. Πρόκειται για εναποθέσεις ασβεστίου στο μαστό με τη διάμετρός τους να κυμαίνεται από 0.1 έως 1 mm και εμφανίζονται είτε μόνες είτε σε ομάδες. Η ακριβής τμηματοποίηση (segmentation) των μικροαποτιτανώσεων στη μαστογραφία συνεισφέρει στην εξαγωγή αξιόπιστων χαρακτηριστικών μορφολογίας, που χρησιμοποιούνται στην αυτόματη διάγνωση με υπολογιστή (Computer-aided Diagnosis, CADx). Στα πλαίσια της παρούσης διδακτορικής διατριβής προτείνεται μία νέα μέθοδος τμηματοποίησης μικροαποτιτανώσεων, η οποία αρχικά εντοπίζει σημεία του περιγράμματος αυτών. Αυτό επιτυγχάνεται με την εφαρμογή της μεθόδου ενεργών ακτίνων (Active Rays), πολικός μετασχηματισμός ενεργών περιγραμμάτων (Active Contours), σε 8 διευθύνσεις και σε δύο κλίμακες του μετασχηματισμού κυματίων (wavelet transform) με φίλτρα Β-spline. Ακολούθως, χρησιμοποιείται μέθοδος επέκτασης περιοχής (region growing) για τον ακριβή προσδιορισμό του περιγράμματος της μικροαποτιτάνωσης. Ως κριτήριο για την αύξηση της περιοχής χρησιμοποιήθηκαν τα σημεία στο περίγραμμα της μικροαποτιτάνωσης, όπως αυτά προσδιορίσθηκαν από τη μέθοδο των ενεργών ακτίνων. Επίσης, υλοποιήθηκε μέθοδος ακτινικής βάθμωσης, η οποία έχει πρόσφατα προταθεί στη βιβλιογραφία για την τμηματοποίηση μικροαποτιτανώσεων, και χρησιμοποιήθηκε για συγκριτική αξιολόγηση. Οι δύο μέθοδοι τμηματοποίησης εφαρμόστηκαν σε 149 ομάδες μικροαποτιτανώσεων, κυρίως πλειόμορφων, που αντλήθηκαν από 130 μαστογραφικές εικόνες από τη βάση DDSM (Digital Database for Screening Mammography). Η ακρίβεια τμηματοποίησης των δύο μεθόδων αξιολογήθηκε από τρεις ακτινολόγους με χρήση 5-βάθμιας κλίμακας. Η ακρίβεια τμηματοποίησης της προτεινόμενης μεθόδου βρέθηκε ίση με 3.96±0.77, 3.97±0.80 και 3.83±0.89, όπως αξιολογήθηκε από κάθε ακτινολόγο, και 2.91±0.86, 2.10±0.94 και 2.56±0.76 για την συγκρινόμενη μέθοδο. Οι διαφορές στην ακρίβεια τμηματοποίησης των δύο μεθόδων ήταν στατιστικώς σημαντικές (Wilcoxon signed-ranks test, p<0.05). Επίσης, μελετήθηκε η επίδραση των δύο μεθόδων τμηματοποίησης στην απόδοση μεθόδου αυτόματης διάγνωσης (χαρακτηρισμό) ομάδων μικροαποτιτανώσεων με υπολογιστή. Η μέθοδος αυτόματης διάγνωσης στηρίζεται σε επιβλεπόμενη ταξινόμηση προτύπων χαρακτηριστικών σχήματος ομάδας αποτιτανώσεων. Συγκεκριμένα, χρησιμοποιήθηκε ταξινομητής ελαχίστων τετραγώνων – ελάχιστης απόστασης και εξήχθησαν χαρακτηριστικά ομοιότητας και διαφοροποίησης (variability) ομάδας μικροαποτιτανώσεων, τα οποία περιγράφουν τη μορφολογία μεμονωμένων αποτιτανώσεων (εμβαδόν, μέγιστη διάμετρος, σχετική αντίθεση). Η απόδοση ταξινόμησης αποτιμήθηκε μέσω εμβαδού καμπύλης παρατηρητών (ROC). Τα χαρακτηριστικά Εμβαδού και μέγιστης Διαμέτρου επέδειξαν σημαντικά υψηλή απόδοση ταξινόμησης (Mann-Whitney U-test, p<0.05) όταν εξήχθησαν από μικροαποτιτανώσεις τμηματοποιημένες με την προτεινόμενη μέθοδο ενεργών ακτίνων (0.82±0.06 και 0.86±0.05, αντίστοιχα). Η απόδοση ταξινόμησης χαρακτηριστικών που εξήχθησαν με μέθοδο τμηματοποίησης ακτινικής βάθμωσης ήταν 0.71±0.08 και 0.75±0.08, αντίστοιχα. Συμπερασματικά, η προτεινόμενη μέθοδος επέδειξε βελτιωμένη ακρίβεια τμηματοποίησης, εκπληρώνοντας ποιοτικά κριτήρια και ενισχύοντας την ικανότητα χαρακτηρισμού των ομάδων αποτιτανώσεων με ανάλυση μορφολογίας (μεγέθους και σχήματος) μεμονωμένων αποτιτανώσεων. Οι περιορισμοί της προτεινόμενης μεθόδου τμηματοποίησης αποδίδονται κυρίως: • Στην ανάλυση δύο κλιμάκων του μετασχηματισμού κυματίου, με αποτέλεσμα τον περιορισμό της προσαρμοστικότητας της μεθόδου σε μικροαποτιτανώσεις διαφορετικών μεγεθών. • Στην μέθοδο επέκτασης περιοχής περιοριζόμενη από σημεία περιγράμματος σε 8 διευθύνσεις. Οι περιορισμοί της αξιολόγησης της προτεινόμενης μεθόδου τμηματοποίησης αποδίδονται κυρίως: • Στην ποιοτική μόνο αξιολόγηση της ακρίβειας τμηματοποίησης, μέσω ανάλυσης παρατηρητών. • Στην χρήση περιορισμένου αριθμού χαρακτηριστικών μορφολογίας στο σύστημα αυτόματης διάγνωσης. Για την αντιμετώπιση των προαναφερθέντων περιορισμών, προτάθηκε η μέθοδος Ενεργών Περιγραμμάτων Πολλαπλών Κλιμάκων με αρχικοποίηση Ενεργών Ακτίνων στην αυτόματα επιλεγόμενη αδρή κλίμακα κυματίου. Αρχικά, χρησιμοποιήθηκε ο μετασχηματισμός συνεχούς κυματίου για την παροχή πολλαπλών κλιμάκων ανάλυσης. Στο πεδίο των πολλαπλών κλιμάκων εντοπίζεται η βέλτιστη αδρή κλίμακα (coarse scale) ανάλυσης με βάση τη μέγιστη απόκριση περιοχής μικροαποτιτάνωσης (scale-space MC signature). Στη συγκεκριμένη βέλτιστη κλίμακα απόκρισης εφαρμόζεται η μέθοδος των ενεργών ακτίνων για τον εντοπισμό σημείων του περιγράμματος της μικροαποτιτάνωσης σε 8 διευθύνσεις. Από αυτά τα σημεία ορίζεται πλήρως το περίγραμμα με χρήση μεθόδου γραμμικής παρεμβολής στη βέλτιστη κλίμακα απόκρισης. Κάθε σημείο του περιγράμματος ακολουθεί την κατεύθυνση μεγιστοποίησης της βάθμωσης εικόνας για τον καθορισμό του περιγράμματος στην βέλτιστη κλίμακα (directional Active Contour). Για την τελική εξαγωγή του περιγράμματος, οι θέσεις των σημείων του περιγράμματος επανακαθορίζονται στις κλίμακες μεγαλύτερης ακρίβειας (fine scales). Η ακρίβεια τμηματοποίησης της δεύτερης προτεινόμενης μεθόδου αξιολογήθηκε ποσοτικά με το κριτήριο επικάλυψης περιοχής. Για το σκοπό αυτό χρησιμοποιούνται τμηματοποιήσεις από ειδικευμένο ακτινολόγο. Τμηματοποιήθηκαν συνολικά 1157 μεμονωμένες μικροαποτιτανώσεις προερχόμενες από 128 ομάδες μικροαποτιτανώσεων, ψηφιοποιημένες σε ανάλυση 50μm (βάση δεδομένων DDSM). Μελετήθηκε επίσης η επίδραση της ακρίβειας τμηματοποίησης της δεύτερης προτεινόμενης μεθόδου στην απόδοση μεθόδου αυτόματης διάγνωσης ομάδων αποτιτανώσεων με βάση χαρακτηριστικά ομοιότητας και διαφοροποίησης (variability) ομάδας μικροαποτιτανώσεων, τα οποία περιγράφουν τη μορφολογία μεμονωμένων αποτιτανώσεων (εμβαδού: εμβαδόν, μέγιστη διάμετρος, σχετική αντίθεση, εκκεντρότητα, συμπαγότητα, διακύμανση ακτινικών αποστάσεων, περιοχής: ροπές 1ης και 2ης τάξης, και περιγράμματος: χαρακτηριστικό ροπής και συχνότητας). Ακολούθως, τέσσερα συστήματα αυτόματης διάγνωσης σχεδιάστηκαν βασιζόμενα στον ταξινομητή ελαχίστων τετραγώνων – ελάχιστης απόστασης και μορφολογικά χαρακτηριστικά εξήχθησαν από τις τρεις αυτόματες μεθόδους τμηματοποίησης (δύο προτεινόμενες και μία συγκρινόμενη). Η ποσοτική αξιολόγηση των προτεινόμενων μεθόδων τμηματοποίησης με χρήση δείκτη επικάλυψης περιοχής απέδειξε ότι μόνο η μέθοδος των Ενεργών Περιγραμμάτων Πολλαπλών Κλιμάκων με αρχικοποίηση Ενεργών Ακτίνων στη βέλτιστη κλίμακα ανάλυσης χαρακτηρίζεται από εξίσου υψηλή απόδοση για τις μικρού (<500μm) και μεγάλου (>500μm) μεγέθους μικροαποτιτανώσεις. Επιπλέον, ο ταξινομητής που βασίστηκε σε χαρακτηριστικά εξαγόμενα από τη βελτιστοποιημένη μέθοδο τμηματοποίησης παρουσίασε καλύτερη απόδοση ταξινόμησης (0.779±0.041) από τους ταξινομητές που βασίστηκαν σε χαρακτηριστικά εξαγόμενα από τη μέθοδο Ενεργών Ακτίνων (0.667±0.041) και τη μέθοδο ακτινικής βάθμωσης (0.670±0.044). Η απόδοση ταξινόμησης του βελτιωμένου αλγόριθμου τμηματοποίησης ήταν δε παρόμοια με την απόδοση του ταξινομητή που βασίστηκε σε χαρακτηριστικά εξαγόμενα από χειροκίνητα τμηματοποιημένες μικροαποτιτανώσεις (0.813±0.037). / Accurate segmentation of microcalcifications (MCs) in mammography is crucial for the quantification of morphologic properties by features incorporated in computer-aided diagnosis (CADx) schemes. At first, a novel segmentation method is proposed implementing active rays (polar-transformed active contours) on B-spline wavelet representation to identify microcalcification contour point estimates in a coarse-to-fine strategy at two levels of analysis. An iterative region growing method is used to delineate the final microcalcification contour curve, with pixel aggregation constrained by the microcalcification contour point estimates. A radial gradient method, representing the current state-of-the-art, was also implemented for comparative purposes. The methods were tested on a dataset consisting of 149 mainly pleomorphic microcalcification clusters originating from 130 mammograms of the DDSM database. Segmentation accuracy of both methods was evaluated by three radiologists, based on a 5-point rating scale. The radiologists’ average accuracy ratings were 3.96±0.77, 3.97±0.80 and 3.83±0.89 for the proposed method, and 2.91±0.86, 2.10±0.94 and 2.56±0.76 for the radial gradient-based method, respectively, while the differences in accuracy ratings between the two segmentation methods were statistically significant (Wilcoxon signed-ranks test, p<0.05). The effect of the two segmentation methods in the classification of benign from malignant microcalcification clusters was also investigated. A Least Square Minimum Distance (LSMD) classifier was employed based on cluster features reflecting three morphological properties of individual microcalcifications (area, length and relative contrast). Classification performance was evaluated by means of the area under ROC curve (Az). The area and length morphologic features demonstrated a statistically significant (Mann-Whitney U-test, p<0.05) higher patient-based classification performance when extracted from microcalcifications segmented by the proposed method (0.82±0.06 and 0.86±0.05, respectively), as compared to segmentation by the radial gradient-based method (0.71±0.08 and 0.75±0.08). The proposed method demonstrates improved segmentation accuracy, fulfilling human visual criteria, and enhances the ability of morphologic features to characterize microcalcification clusters. The limitations of this method could be attributed to: • Multiscale analysis restricted to two scales and ad-hoc selection of the coarsest scale of analysis, limiting the desired size-adaptation property of the proposed segmentation method. • Use of constrained region growing to delineate the final MC region to avoid discontinouities inherent to the 8-contour point estimates. • Segmentation accuracy assessed only qualitatively. • Limited morphology anaysis incorporated into the CADx framework. To overcome these limitations, a second method is introduced adaptive to coarse scale selection to initialize the segmentation algorithm, by means of scale-space signatures. Also, we suggest the analysis in the continuous wavelet transform offering a rich multiscale frame. In this framework, multiscale active contours are introduced, utilizing as initial step the previously proposed Active Rays method combined to linear interpolation, for initial contour estimation. Then, each contour point follows the direction where the gradient is maximized. MCs are finally delineated by refining each contour point position at finer scales more accurately. Segmentation accuracy is quantitatively assessed by means of the Area Overlap Measure, utilizing manual segmentation of individual MCs as ground truth, provided by expert radiologists. A total of 1157 individual MCs were segmented in a dataset of 128 MC clusters, digitized at 50μm pixel resolution. To further ensure feature reliability, features extracted from the improved segmentation method were compared (Pearson correlation) to features extracted from manual experts’ delineations. Following, four CADx schemes were implemented utilizing Least Square Minimum Distance (LSMD) classifier and morphology features extracted from the two proposed and the Radial Gradient method. Training of all classifiers was accoblished by features extracted from manually segmented MCs. Quantitative analysis indicated that Multiscale Active Contour method initialized by Active Rays (MAC-AR) had similar Area Overlap Measure performance either for small and large MCs. Furthermore, the improved method demonstrated better performance in terms of classification performance (Az=0.78), as compared to Multiscale Active Rays constrained Region Growing (MAR-RG) (Az=0.67) and the radial gradient one (Az=0.67), however, statistically similar to manual segmentation, representing the best performance (Az=0.81).

Neste trabalho é proposto um sistema computacional para auxílio ao diagnóstico de câncer de mama com microcalcificações associadas. O sistema é composto por 3 etapas principais: segmentação, extração de atributos e classificação. Na etapa de segmentação, a região suspeita do mamograma digitalizado (ROI - região de interesse) é processada para isolar as microcalcificações das estruturas normais da imagem. O resultado final é uma imagem binária contendo apenas as microcalcificações. Nesta etapa são utilizadas três técnicas combinadas: linearização do histograma da imagem (\"stretching\"), imagem-diferença e \"thresholding\" adaptativo. Na etapa de extração de atributos, são realizadas 34 medidas: 13 medidas de textura, calculadas da ROI da imagem não segmentada, 19 medidas de forma das microcalcificações, 1 medida de distribuição e 1 de quantidade das microcalcificações, calculadas da ROI da imagem segmentada. A partir dos métodos erro de Bayes e distância JM, são separados os 6 melhores atributos para compor o vetor de atributos utilizado na classificação. Na etapa de classificação, são avaliadas três diferentes classificadores: Regra de Bayes (método paramétrico), k-NN (método não-paramétrico) e Rede Neural Artificial - tipo MLP (Perceptron multi-camadas). Os classificadores são treinados e testados com diferentes grupos de amostras, utilizando a técnica \"leave-k-out\". Por fim, os resultados obtidos em cada etapa são apresentados e discutidos a partir de tabelas e curvas ROC. / In the present work, a computerized system has been proposed to aid in the diagnosis of breast cancer with associated microcalcifications. The system is composed of 3 main stages: segmentation, features extraction, and classification. In the segmentation stage, the suspected region in the digitized mammogram is processed to isolate the microcalcifications from the normal structures of the image. The final result is a binary image which has only microcalcifications. At this stage three combined techniques have been used: the stretching method, the image-difference and a thresholding adaptive method. At the feature extraction stage, 34 measurements were implemented: 13 of texture, calculated from the ROI of the original image, 19 of shape, 1 of distribution and 1 measure related of the number of the microcalcification. To compose the feature space, a subset of the six best features were evaluated using the Bayes error and Jeffreis-Matusita methods. In the classification stage, three classifiers were evaluated: the Bayes roles (parametric method), the k-Nearest Neighbour (non-parametric method), and a MLP (Multi-Iayer perceptron) Artifitial Neural Network. The classifiers were trained and tested with different sample groups using the leave-k-out method. The final results obtained at each stage are shown and discussed using the receiver operating characteristic (ROC) curves and tables.

Microcalcification (MC) detection is an important component of breast cancer diagnosis. However, visual analysis of mammograms is a difficult task for radiologists. Computer Aided Diagnosis (CAD) technology helps in identifying lesions and assists the radiologist in making his final decision. This work is a part of a CAD project carried out at the Imaging Science Research Division (ISRD), Digital Medical Imaging Program, Moffitt Cancer Research Center, Tampa, FL. A CAD system had been previously developed to perform the following tasks: (a) pre-processing, (b) segmentation and (c) feature extraction of mammogram images. Ten features covering spatial, and morphological domains were extracted from the mammograms and the samples were classified as Microcalcification (MC) or False alarm (False Positive microcalcification/ FP) based on a binary truth file obtained from a radiologist's initial investigation. The main focus of this work was two-fold: (a) to analyze these features, select the most significant features among them and study their impact on classification accuracy and (b) to implement and compare two machine-learning algorithms, Neural Networks (NNs) and Support Vector Machines (SVMs) and evaluate their performances with these features. The NN was based on the Standard Back Propagation (SBP) algorithm. The SVM was implemented using polynomial, linear and Radial Basis Function (RBF) kernels. A detailed statistical analysis of the input features was performed. Feature selection was done using Stepwise Forward Selection (SFS) method. Training and testing of the classifiers was carried out using various training methods. Classifier evaluation was first performed with all the ten features in the model. Subsequently, only the features from SFS were used in the model to study their effect on classifier performance. Accuracy assessment was done to evaluate classifier performance. Detailed statistical analysis showed that the given dataset showed poor discrimination between classes and proved a very difficult pattern recognition problem. The SVM performed better than the NN in most cases, especially on unseen data. No significant improvement in classifier performance was noted with feature selection. However, with SFS, the NN showed improved performance on unseen data. The training time taken by the SVM was several magnitudes less than the NN. Classifiers were compared on the basis of their accuracy and parameters like sensitivity and specificity. Free Receiver Operating Curves (FROCs) were used for evaluation of classifier performance. The highest accuracy observed was about 93% on training data and 76% for testing data with the SVM using Leave One Out (LOO) Cross Validation (CV) training. Sensitivity was 81% and 46% on training and testing data respectively for a threshold of 0.7. The NN trained using the 'single test' method showed the highest accuracy of 86% on training data and 70% on testing data with respective sensitivity of 84% and 50%. Threshold in this case was -0.2. However, FROC analyses showed overall superiority of SVM especially on unseen data. Both spatial and morphological domain features were significant in our model. Features were selected based on their significance in the model. However, when tested with the NN and SVM, this feature selection procedure did not show significant improvement in classifier performance. It was interesting to note that the model with interactions between these selected variables showed excellent testing sensitivity with the NN classifier (about 81%). Recent research has shown SVMs outperform NNs in classification tasks. SVMs show distinct advantages such as better generalization, increased speed of learning, ability to find a global optimum and ability to deal with linearly non-separable data. Thus, though NNs are more widely known and used, SVMs are expected to gain popularity in practical applications. Our findings show that the SVM outperforms the NN. However, its performance depends largely on the nature of data used.

Neste trabalho é proposto um sistema computacional para auxílio ao diagnóstico de câncer de mama com microcalcificações associadas. O sistema é composto por 3 etapas principais: segmentação, extração de atributos e classificação. Na etapa de segmentação, a região suspeita do mamograma digitalizado (ROI - região de interesse) é processada para isolar as microcalcificações das estruturas normais da imagem. O resultado final é uma imagem binária contendo apenas as microcalcificações. Nesta etapa são utilizadas três técnicas combinadas: linearização do histograma da imagem (\"stretching\"), imagem-diferença e \"thresholding\" adaptativo. Na etapa de extração de atributos, são realizadas 34 medidas: 13 medidas de textura, calculadas da ROI da imagem não segmentada, 19 medidas de forma das microcalcificações, 1 medida de distribuição e 1 de quantidade das microcalcificações, calculadas da ROI da imagem segmentada. A partir dos métodos erro de Bayes e distância JM, são separados os 6 melhores atributos para compor o vetor de atributos utilizado na classificação. Na etapa de classificação, são avaliadas três diferentes classificadores: Regra de Bayes (método paramétrico), k-NN (método não-paramétrico) e Rede Neural Artificial - tipo MLP (Perceptron multi-camadas). Os classificadores são treinados e testados com diferentes grupos de amostras, utilizando a técnica \"leave-k-out\". Por fim, os resultados obtidos em cada etapa são apresentados e discutidos a partir de tabelas e curvas ROC. / In the present work, a computerized system has been proposed to aid in the diagnosis of breast cancer with associated microcalcifications. The system is composed of 3 main stages: segmentation, features extraction, and classification. In the segmentation stage, the suspected region in the digitized mammogram is processed to isolate the microcalcifications from the normal structures of the image. The final result is a binary image which has only microcalcifications. At this stage three combined techniques have been used: the stretching method, the image-difference and a thresholding adaptive method. At the feature extraction stage, 34 measurements were implemented: 13 of texture, calculated from the ROI of the original image, 19 of shape, 1 of distribution and 1 measure related of the number of the microcalcification. To compose the feature space, a subset of the six best features were evaluated using the Bayes error and Jeffreis-Matusita methods. In the classification stage, three classifiers were evaluated: the Bayes roles (parametric method), the k-Nearest Neighbour (non-parametric method), and a MLP (Multi-Iayer perceptron) Artifitial Neural Network. The classifiers were trained and tested with different sample groups using the leave-k-out method. The final results obtained at each stage are shown and discussed using the receiver operating characteristic (ROC) curves and tables.

Ziel: Untersuchung des Stellenwertes der HR-MRT bei Frauen mit suspekten Mikroverkalkungen der Kategorie BIRADS 4 und 5, die im Rahmen des Mammographie-Screenings, der weiteren Diagnostik oder der Nachsorge nach brusterhaltender Karzinomtherapie detektiert wurden. Methoden: Retrospektive Studie von Mai 2003 bis Mai 2014. 139 Frauen mit mammographisch nachgewiesenen Mikroverkalkungen und Anfertigung einer hochauflösenden Mamma-MRT sowie stereotaktischer Vakuumstanzbiopsie oder primär offener Exzision. Ergebnisse: Bei 52 Patientinnen wurde ein Mammakarzinom diagnostiziert. 34 DCIS (65%), 2 DCIS (4%) mit minimal invasiver Komponente (pTmic) und 18 invasive Karzinome (31%). In der Mammographie wurden die Kalzifikationen in 80% (111/139) BIRADS-Kategorie 4 und in 20% (27/139) BIRADS-Kategorie 5 zugeordnet. In der HR-Mamma-MRT wurden knapp 40% (55/139) der mammographisch suspekten Mikroverkalkungen ebenfalls als malignomverdächtig eingestuft, davon 70% (38/55) in BIRADS 4 und 30% (17/55) in BIRADS 5. Unter Berücksichtigung des vorselektionierten Studienkollektivs ergibt sich Folgendes: Der PPV der Mammographie betrug 37% (52/139). Der PPV und NPV des HR-MRT betrug 76% (42/55) und 88% (74/84). Das MRT wies eine Sensitivität von 80% (42/52) und eine Spezifität von 85% (74/87) auf. Schlussfolgerung: Bei mammographisch suspekten Mikroverkalkungen der Kategorie BIRADS 4 und 5 ist die hochauflösende Mamma-MRT in der Lage das Karzinomrisiko vor bioptischer Abklärung einzuordnen. Die artefaktfreie HR-MRT kann somit die Anzahl unnötiger Biopsien deutlich reduzieren, sofern falsch negative (fast ausnahmslos Frühstkarzinome) und falsch positive Befunde akzeptiert werden.

Currently, mammography is the most common imaging technology used in breast screening. Low dose X-rays are passed through the breast to generate images called mammograms. One type of breast abnormality is a cluster of microcalcifications. Usually, in benign cases, microcalcifications result from the death of fat cells or are due to secretion by the lobules. However, in some cases, clusters of microcalcifications are indicative of early breast cancer, partly because of the secretions by cancer cells or the death of such cells. Due to the different attenuation characteristics of normal breast tissue and microcalcifications, the latter ideally appear as bright white spots and this allows detection and analysis for breast cancer classification. Microcalcification detection is one of the primary foci of screening and has led to the development of computer-aided detection (CAD) systems. However, a fundamental limitation of mammography is that it gives a 2D view of the tightly compressed 3D breast. The depths of entities within the breast are lost after this imaging process, even though the breast tissue is spread out as a result of the compression force applied to the breast. The superimposition of tissues can occlude cancers and this has led to the development of digital breast tomosynthesis (DBT). DBT is a three-dimensional imaging involving an X-ray tube moving in an arc around the breast, over a limited angular range, producing multiple images, which further undergo a reconstruction step to form a three-dimensional volume of breast. However, reconstruction remains the subject of research and small microcalcifications are "smeared" in depth by current algorithms, preventing detailed analysis of the geometry of a cluster. By using the geometry of the DBT acquisition system, we derive the "epipolar" trajectory of a microcalcification. As a first application of the epipolars, we develop a clustering algorithm after using the Hough transform to find corresponding points generated from a microcalcification. Noise points can also be isolated. In addition, we show how microcalcification projections can be detected adaptively. Epipolar analysis has also led to a novel detection algorithm for DBT using a Bayesian method, which estimates a maximum a posterior (MAP) labelling in each individual image and subsequently for all projections iteratively. Not only does this algorithm output the binary decision of whether a pixel is a microcalcification, it can predict the approximate depth of the microcalcification in the breast if it is. Based on the epipolar analysis, reconstruction of just a region of interest (ROI) e.g. microcalcification clusters is possible and it is more straightforward than any existing method using reconstruction slices. This potentially enables future classification of breast cancer when more clinical data becomes available.

O objetivo principal deste projeto foi desenvolver um modelo para a detecção de clusters de microcalcificações para o processamento de imagens mamográficas inteiras. O modelo foi subdividido em três etapas sendo na primeira realizado um pré-processamento para a melhoria da qualidade das imagens mamográficas no que se refere à remoção de ruídos e alargamento de contraste. Na segunda etapa do processamento, um conjunto de algoritmos foi aplicado visando-se a detecção propriamente dita de regiões de interesse nas imagens as quais possivelmente representariam os agrupamentos de microcalcificações. A terceira etapa destinou-se à classificação das regiões pré-selecionadas na etapa anterior para a determinação final dos achados verdadeiro-positivos (VP), buscando-se, assim, a diminuição da taxa de achados falso-positivos (FP). Em cada etapa do desenvolvimento do modelo, testes computacionais foram realizados a fim de auxiliarem na análise de resultados preliminares. Por fim, vários testes computacionais foram realizados em três conjuntos de imagens com composições distintas sendo o primeiro formado por regiões de interesse (RI) de phantoms, o segundo por RI de mamografias e o terceiro por imagens mamográficas inteiras. Propõe-se a integração das técnicas propostas ao sistema CAD em desenvolvimento pelo grupo de pesquisa do LAPIMO (Laboratório de Análise e Processamento de Imagens Médicas e Oftalmológicas) da Escola de Engenharia de São Carlos do presente instituto. / The main purpose of this project was to develop a new model for the detection of microcalcifications clusters for image processing in full mammograms. The model was subdivided in three stages being in the first accomplished a pre-processing for the improvement of the quality of the mammographic images through the removal of noise and contrast enlargement. In the second stage of the processing, a group of algorithms was applied being sought the detection properly said of regions of interest (ROI\'s) in the images which possibly would represent the microcalcifications clusters. The third stage was destined to the classification of the pre-selected areas in the previous stage for the final determination of the true-positive findies (TP), being looked for, like this, the decrease of the rate of false-positive (FP) ones. In each stage of the development of the model, computational tests was accomplished in order to analyze the preliminary results. Finally, several computational tests was accomplished in three groups of images with different compositions being the first formed by ROI\'s of phantoms, the second by ROI\'s of mammograms and the third for full mammograms. Is proposed too the integration of the techniques proposed to the CAD scheme in development for the group of research of LAPIMO (Laboratory of Analysis and Processing of Medical and Ophthalmology Images) of the University of São Paulo, São Carlos of the present institute.

O objetivo principal deste projeto foi desenvolver um modelo para a detecção de clusters de microcalcificações para o processamento de imagens mamográficas inteiras. O modelo foi subdividido em três etapas sendo na primeira realizado um pré-processamento para a melhoria da qualidade das imagens mamográficas no que se refere à remoção de ruídos e alargamento de contraste. Na segunda etapa do processamento, um conjunto de algoritmos foi aplicado visando-se a detecção propriamente dita de regiões de interesse nas imagens as quais possivelmente representariam os agrupamentos de microcalcificações. A terceira etapa destinou-se à classificação das regiões pré-selecionadas na etapa anterior para a determinação final dos achados verdadeiro-positivos (VP), buscando-se, assim, a diminuição da taxa de achados falso-positivos (FP). Em cada etapa do desenvolvimento do modelo, testes computacionais foram realizados a fim de auxiliarem na análise de resultados preliminares. Por fim, vários testes computacionais foram realizados em três conjuntos de imagens com composições distintas sendo o primeiro formado por regiões de interesse (RI) de phantoms, o segundo por RI de mamografias e o terceiro por imagens mamográficas inteiras. Propõe-se a integração das técnicas propostas ao sistema CAD em desenvolvimento pelo grupo de pesquisa do LAPIMO (Laboratório de Análise e Processamento de Imagens Médicas e Oftalmológicas) da Escola de Engenharia de São Carlos do presente instituto. / The main purpose of this project was to develop a new model for the detection of microcalcifications clusters for image processing in full mammograms. The model was subdivided in three stages being in the first accomplished a pre-processing for the improvement of the quality of the mammographic images through the removal of noise and contrast enlargement. In the second stage of the processing, a group of algorithms was applied being sought the detection properly said of regions of interest (ROI\'s) in the images which possibly would represent the microcalcifications clusters. The third stage was destined to the classification of the pre-selected areas in the previous stage for the final determination of the true-positive findies (TP), being looked for, like this, the decrease of the rate of false-positive (FP) ones. In each stage of the development of the model, computational tests was accomplished in order to analyze the preliminary results. Finally, several computational tests was accomplished in three groups of images with different compositions being the first formed by ROI\'s of phantoms, the second by ROI\'s of mammograms and the third for full mammograms. Is proposed too the integration of the techniques proposed to the CAD scheme in development for the group of research of LAPIMO (Laboratory of Analysis and Processing of Medical and Ophthalmology Images) of the University of São Paulo, São Carlos of the present institute.

Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-10-06T13:19:49Z No. of bitstreams: 1 DissAOS.pdf: 12231590 bytes, checksum: 0d013d4adc46d9e7c5828719c8c6951f (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-13T20:24:59Z (GMT) No. of bitstreams: 1 DissAOS.pdf: 12231590 bytes, checksum: 0d013d4adc46d9e7c5828719c8c6951f (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-13T20:25:09Z (GMT) No. of bitstreams: 1 DissAOS.pdf: 12231590 bytes, checksum: 0d013d4adc46d9e7c5828719c8c6951f (MD5) / Made available in DSpace on 2016-10-13T20:25:19Z (GMT). No. of bitstreams: 1 DissAOS.pdf: 12231590 bytes, checksum: 0d013d4adc46d9e7c5828719c8c6951f (MD5) Previous issue date: 2016-02-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Mammography is considered the “gold standard"in the early detection of breast cancer, being this disease one of the greatest health problems of women worldwide. Clustered microcalcifications detected on mammograms are very important findings in asymptomatic patients with early breast cancer and may be considered one of the first signs of malignancy. However, due to the small size of these structures, associated with the visual fatigue of radiologists resulting from the analysis of a large volume of images, clinical studies indicate that from 10 to 30% of microcalcifications presented in mammograms are lost during diagnosis. Within this scenario, this master thesis aims to develop an automatic system for the detection of clustered microcalcifications in digitized mammography images. To solve this problem, we use the transformed dua-three complex wavelet to detect the microcalsifications since this technique has some important characteristics for the signal analysis, for instance, good directional selectivity, approximate shift invariance and it provides both information - magnitude and phase. After the detection of isolated microcalcifications, a post-processing step is used to automatically demarcate regions containing clusters of microcalcifications. Furthermore, three techniques were investigated for the analysis of each clustered detection in order to identify false-positive clusters, such as: the Hessian matrix, the groups exclusion and the gray level co-occurrence matrix technique and SVM classifiers. For the development and testing of the algorithms one digitized mammogram database were used. The analysis of the results were performed by using ROC and FROC curves. The method achieved good results when compared to the mark made by experts. / A mamografia é considerada o "padrão ouro"na detecção precoce do câncer de mama, sendo essa doença um dos maiores problemas de saúde da mulher no mundo. Agrupamentos de microcalcificações detectados nos mamogramas são achados muito importantes em pacientes assintomáticas com câncer de mama e podem representar o primeiro sinal de malignidade. No entanto, devido ao reduzido tamanho dessas estruturas, associado à fadiga visual dos radiologistas resultante da análise de grandes volumes de imagens, estudos clínicos indicam que de 10 a 30% das microcalcificações presentes nos mamogramas são perdidas durante o diagnóstico. Diante deste quadro, este trabalho de mestrado tem por objetivo o desenvolvimento de um sistema automático para a detecção de agrupamentos de microcalcificações em imagens de mamogramas digitalizados. Para isso, utilizou-se a transformada wavelet complexa de árvore dupla (DT-CWT) para a detecção das microcalcificações, visto que essa técnica possui características importantes para a análise de sinais como, por exemplo, boa seletividade direcional, invariância aproximada ao deslocamento e fornece ambas informações – magnitude e fase. Após a detecção das microcalcificações isoladas, uma etapa de pós-processamento foi utilizada para demarcar automaticamente regiões contendo agrupamentos de microcalcificações. Além disso, três técnicas foram investigadas para a análise de cada agrupamento detectado, com o intuito de identificar agrupamentos falsopositivos, sendo elas: a matriz Hessiana, a exclusão de agrupamentos e a técnica de matriz de coocorrência de níveis de cinza e classificadores SVMs. Uma base de dados de mamogramas digitalizados foi utilizada para o desenvolvimento e testes dos algoritmos. A análise dos resultados foi realizada usando curvas ROC e FROC. O método obteve bons resultados quando comparado às marcações realizadas por especialistas e presentes na base de dados.

O presente trabalho consiste na investigação das características da imagem mamográfica digitalizada para definir automaticamente parâmetros de processamento em um esquema de diagnóstico auxiliado por computador (CAD) para mamografia, com o objetivo de se obter o melhor desempenho possível. Além disso, com base na aplicação dos resultados dessa primeira investigação, propõe-se também uma técnica de redução dos índices de falso-positivo em esquemas CAD visando à redução do número de biópsias desnecessárias. Para a definição automática dos parâmetros de processamento nas técnicas de detecção de microcalcificações e nódulos, foram extraídas algumas características das imagens, como desvio padrão, terceiro momento e o limiar de binarização. Utilizando o método de automatização proposto, observou-se um aumento de 20% no desempenho do esquema CAD (Az da curva ROC) em relação ao método não automatizado com parâmetro fixo. Para que fosse possível o processamento da imagem mamográfica inteira pelo esquema CAD e as técnicas desenvolvidas, foi desenvolvida também uma técnica para seleção automática de regiões de interesses, que recorta partes relevantes da mama para a segmentação. O índice de falsos positivos foi tratado por técnica específica desenvolvida com base na comparação das duas incidências típicas do exame mamográfico que, juntamente com a avaliação automática da imagem no pré-processamento para detecção de microcalcificações produziu uma redução significativa de 86% daquela taxa em relação ao procedimento de parâmetro fixo. / This present work consists on the investigation of mammographic image characteristics for automatic determination of image processing parameters for a mammography computer aided diagnosis scheme (CAD) in order to get optimal performance. Additionally, using the results obtained on this first investigation, it was also developed a new technique for the reduction of false-positive rates on CAD projects, which can result on the reduction of the number of unnecessary biopsies. For the automatic definition of the image processing parameters for the techniques of detection of microcalcifications and nodules, some image characteristics had been extracted, as standard deviation, third momentum and the thresholding value. Using the proposed automatization method it was reported an increase of 20% in the CAD performance (evaluated determining the ROC curve) in comparison to the non-automatic method (fixed parameter). Besides, for CAD schemes it is necessary to process the entire mammographic image. Thus, it was also developed a technique for automatic selection of regions of interests in the mammogram, which extracts better regions from breast image for further segmentation. False-positives rates was treated by a specific technique based on the comparison of the two typical incidences of mammographic examination that together with the automatic parameter determination method for microcalcification detection produced a significant reduction of 86% of that rate in relation to the procedure that uses fixed parameter.