Caracterización de Patrones Anormales en Mamografías

Abstract. Computer-guided image interpretation is an extensive research area whose main purpose is to provide tools to support decision-making, for which a large number of automatic techniques have been proposed, such as, feature extraction, pattern recognition, image processing, machine learning, among others. In breast cancer, the results obtained at this area, they have led to the development of diagnostic support systems, which have even been approved by the FDA (Federal Drug Administration). However, the use of those systems is not widely extended in clinic scenarios, mainly because their performance is unstable and poorly reproducible. This is due to the high variability of the abnormal patterns associated with this neoplasia. This thesis addresses the main problem associated with the characterization and interpretation of breast masses and architectural distortion, mammographic findings directly related to the presence of breast cancer with higher variability in their form, size and location. This document introduces the design, implementation and evaluation of strategies to characterize abnormal patterns and to improve the mammographic interpretation during the diagnosis process. The herein proposed strategies allow to characterize visual patterns of these lesions and the relationship between them to infer their clinical significance according to BI-RADS (Breast Imaging Reporting and Data System), a radiologic tool used for mammographic evaluation and reporting. The obtained results outperform some obtained by methods reported in the literature both tasks classification and interpretation of masses and architectural distortion, respectively, demonstrating the effectiveness and versatility of the proposed strategies.Resumen. La interpretación de imágenes guiada por computador es una área extensa de investigación cuyo objetivo principal es proporcionar herramientas para el soporte a la toma de decisiones, para lo cual se han usado un gran número de técnicas de extracción de características, reconocimiento de patrones, procesamiento de imágenes, aprendizaje de máquina, entre otras. En el cáncer de mama, los resultados obtenidos en esta área han dado lugar al desarrollo de sistemas de apoyo al diagnóstico que han sido incluso aprobados por la FDA (Federal Drug Administration). Sin embargo, el uso de estos sistemas no es ampliamente extendido, debido principalmente, a que su desempeño resulta inestable y poco reproducible frente a la alta variabilidad de los patrones anormales asociados a esta neoplasia. Esta tesis trata el principal problema asociado a la caracterización y análisis de masas y distorsión de la arquitectura debido a que son hallazgos directamente relacionados con la presencia de cáncer y que usualmente presentan mayor variabilidad en su forma, tamaño y localización, lo que altera los resultados diagnósticos. Este documento introduce el diseño, implementación y evaluación de un conjunto de estrategias para caracterizar patrones anormales relacionados con este tipo de hallazgos para mejorar la interpretación y soportar el diagnóstico mediante la imagen mamaria. Los modelos aquí propuestos permiten caracterizar patrones visuales y la relación entre estos para inferir su significado clínico según el estándar BI-RADS (Breast Imaging Reporting and Data System) usado para la evaluación y reporte mamográfico. Los resultados obtenidos han demostrado mejorar a los resultados obtenidos por los métodos reportados en la literatura en tareas como clasificación e interpretación de masas y distorsión arquitectural, demostrando la efectividad y versatilidad de las estrategia propuestas.Doctorad

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Método de detección de distorsiones de la arquitectura de la glándula mamaria a partir de imágenes radiológicas

Este documento de tesis presenta la planeación, implementación y pruebas de un nuevo método que sirve como soporte para la detección de distorsiones de la arquitectura en la glándula mamaria a partir de imágenes de radiología de mama. El método asiste a los especialistas en el proceso de decisión diagnóstica como segundo intérprete en el análisis de mamografías, mediante la integración de cuatro etapas principales: preprocesamiento, detección de regiones de interés que sean candidatas a la posible presencia de distorsión de la arquitectura de la glándula mamaria, extracción y selección de características de las regiones de interés detectadas y finalmente clasificación de esas regiones de interés con base en las características extraídas de las mismas. El método propuesto se valida mediante el análisis de imágenes mamográficas de la base de datos DDSM, logrando valores de precisión general hasta de un 90.7% lo cual lo convierte en una base importante en la búsqueda de la reducción del alto número de diagnósticos errados que conducen a las altas tasas de morbilidad por cáncer de mama que se presentan en el mundo./Abstract. This thesis presents the design, implementation and test of a new method that serves as support for the detection of architectural distortion in the mammary gland from breast radiology images. The method proposed here assists the specialists in the diagnosis of breast cáncer through four main phases: preprocessing, detection of regions of interest that are candidates for the possible presence of architectural distortion of the mammary gland, feature selection and extraction and finally classification of these regions of interest based on the extracted features. The method proposed in this thesis is validated through the analysis of mammographic images from DDSM obtaining values of 90.7% in the overall accuracy. This result is a very important contribution and encourage the research in order to reduce the high number of misdiagnoses that are currently presented and lead to high rates of morbidity from breast cáncer.Maestrí

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered