Improved Diagnostics by Assessing the Micromorphology of Breast Calcifications via X-Ray Dark-Field Radiography

Breast microcalcifications play an essential role in the detection and evaluation of early breast cancer in clinical diagnostics. However, in digital mammography, microcalcifications are merely graded with respect to their global appearance within the mammogram, while their interior microstructure remains spatially unresolved and therefore not considered in cancer risk stratification. In this article, we exploit the sub-pixel resolution sensitivity of X-ray dark-field contrast for clinical microcalcification assessment. We demonstrate that the micromorphology, rather than chemical composition of microcalcification clusters (as hypothesised by recent literature), determines their absorption and small-angle scattering characteristics. We show that a quantitative classification of the inherent microstructure as ultra-fine, fine, pleomorphic and coarse textured is possible. Insights underlying the micromorphological nature of breast calcifications are verified by comprehensive high-resolution micro-CT measurements. We test the determined microtexture of microcalcifications as an indicator for malignancy and demonstrate its potential to improve breast cancer diagnosis, by providing a non-invasive tool for sub-resolution microcalcification assessment. Our results indicate that dark-field imaging of microcalcifications may enhance the diagnostic validity of current microcalcification analysis and reduce the number of invasive procedures

Improved Diagnostics by Assessing the Micromorphology of Breast Calcifications via X-Ray Dark-Field Radiography

Breast microcalcifications play an essential role in the detection and evaluation of early breast cancer in clinical diagnostics. However, in digital mammography, microcalcifications are merely graded with respect to their global appearance within the mammogram, while their interior microstructure remains spatially unresolved and therefore not considered in cancer risk stratification. In this article, we exploit the sub-pixel resolution sensitivity of X-ray dark-field contrast for clinical microcalcification assessment. We demonstrate that the micromorphology, rather than chemical composition of microcalcification clusters (as hypothesised by recent literature), determines their absorption and small-angle scattering characteristics. We show that a quantitative classification of the inherent microstructure as ultra-fine, fine, pleomorphic and coarse textured is possible. Insights underlying the micromorphological nature of breast calcifications are verified by comprehensive high-resolution micro-CT measurements. We test the determined microtexture of microcalcifications as an indicator for malignancy and demonstrate its potential to improve breast cancer diagnosis, by providing a non-invasive tool for sub-resolution microcalcification assessment. Our results indicate that dark-field imaging of microcalcifications may enhance the diagnostic validity of current microcalcification analysis and reduce the number of invasive procedures

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detectio

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic

Prototipo de un sistema de diagnóstico asistido por ordenador orientado a la localización de clusters de microcalcificaciones en mamografías

En este trabajo se presenta la construcción metodológica para la implementación de un prototipo de aplicativo software que sirva como herramienta de apoyo al diagnóstico de cáncer de mama, a partir de las diferentes técnicas de procesamiento de imágenes y modelos de aprendizaje supervisado y no-supervisado. Tiene como aporte fundamental el hecho de que es una metodología que acopla diferentes etapas de procesamiento bastante robustas que permiten hacer un tratamiento desde la imagen mamográfica en crudo hasta la recomendación final dada por el sistema (End-to-End). En particular se consideró la técnica de realce de contraste de corrección gamma adaptativa con ponderación distribuida (AGCWD) y binarización de Otsu para la segmentación del tejido mamario, el segmentador K-means para la identificación del musculo pectoral, una red neuronal convolucional (CNN) para la localización de microcalcificaciones, un ensamble de redes neuronales artificiales (RNA) responsable de la clasificación y del proceso de búsqueda de imágenes similares. Además, se usó la librería tkinter para la implementación de la interfaz gráfica de usuario (GUI) en Python. Para la validación de la metodología se usaron dos bases de datos, The Mammographic Image Analysis (mini-MIAS) y The Digital Database for Screening Mammography (DDSM). Image Analysis (mini-MIAS) y The Digital Database for Screening Mammography (DDSM). Los resultados obtenidos reflejan que esta metodología mejora sustancialmente el rendimiento en la eliminación de artefactos (99.78%), la precisión en la remoción del musculo pectoral (92.14%), la reducción de falsos positivos en la detección de microcalcificaciones (0.47 por imagen), y aumento en el acierto en la clasificación según el estándar BI-RADS (82%) en comparación a otros trabajos en el estado del arte.This work presents the methodological construction for the implementation of a prototype software application that serves as a support tool for breast cancer diagnosis, based on different image processing techniques and supervised and unsupervised learning models. Its fundamental contribution is the fact that it is a methodology that couples different processing stages quite robust that allow a treatment from the raw mammographic image to the final recommendation given by the system (End-to-End). In particular, the contrast enhancement with adaptive gamma correction weighting distribution (AGCWD) and Otsu binarization technique was considered for the segmentation of breast tissue, the K-means segmenter for the identification of pectoral muscle, a convolutional neural network (CNN) for the localization of microcalcifications, an assembly of artificial neural networks (ANN) responsible for the classification and the search process of similar images. In addition, the tkinter library was used for the implementation of the graphical user interface (GUI) in Python. Two databases, The Mammographic Image Analysis (mini-MIAS) and The Digital Database for Screening Mammography (DDSM), were used to validate the methodology. The results obtained reflect that this methodology substantially improves the performance in the elimination of artifacts (99.78%), the accuracy in the removal of the pectoral muscle (92.14%), the reduction of false positives in the detection of microcalcifications (0.47 per image), and the increase in the accuracy in the classification according to the BI RADS standard (82%) in comparison to other works in the state of the artMaestríaMagíster en Ingeniería EléctricaIndice 1. Resumen 1 2. Abstract 2 3. Introducción 3 4. Planteamiento del problema 4 5. Justificación 8 6. Objetivos 9 6.1. General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 6.2. Específicos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 7. Revision del estado del arte 10 7.1. Eliminacion de artefactos y remoción del musculo pectoral . . . . . . 10 7.2. Deteccion de clusters de microcalcificaciones . . . . . . . . . . . . . . 11 7.3. Clasificación de clusters de microcalcificaciones . . . . . . . . . . . . . 12 8. Marco Teórico 14 8.1. Eliminacion de ruido y supresion de artefactos . . . . . . . . . . . . . 15 8.1.1. Realce de contraste por corrección gamma adaptativa con ponderación distribuida . . . . . . 15 8.1.2. Segmentación del tejido mamario . . . . . . . . . . . . . . . . 16 8.2. Remocion del musculo pectoral . . . . . . . . . . . . . . . . . . . . . 18 8.2.1. Segmentacion del musculo pectoral con K-medias . . . . . . . 19 8.2.2. Correccion del contorno con aproximación polinomial . . . . . 20 8.3. Deteccion y localizaci´on de microcalcificaciones . . . . . . . . . . . . 22 8.3.1. Deteccion de MC con CNN . . . . . . . . . . . . . . . . . . . 22 8.3.2. Realce de contraste, segmentación y filtrado de MC . . . . . . 24 8.4. Clasificación de microcalcificaciones según su categoría BI-RADS . . 26 8.4.1. Escala BI-RADS . . . . . . . . . . . . . . . . . . . . . . . . . 26 8.4.2. Extracci´on de características . . . . . . . . . . . . . . . . . . . 27 8.4.3. Redes Neuronales Artificiales . . . . . . . . . . . . . . . . . . 27 8.4.4. Sistema de recuperación de imágenes de microcalcificaciones . 28 9. Marco experimental 30 9.1. Bases de datos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 9.2. Resultados de las pruebas de eliminación de artefactos . . . . . . . . 31 9.3. Resultados de las pruebas de remoción del musculo pectoral . . . . . 32 9.4. Resultados de las pruebas de detección y localización de MC . . . . . 34 9.5. Resultados de las pruebas de clasificacion de MC según su categoría BI-RADS . . . . . . 36 9.6. Diseño de la interfaz del sistema DAO . . . . . . . . . . . . . . . . . 37 10.Conclusiones 41 11.Resultados académicos 44 12.Agradecimientos 45 13.Bibliografía 4