A Decision Support System (DSS) for Breast Cancer Detection Based on Invariant Feature Extraction, Classification, and Retrieval of Masses of Mammographic Images

This paper presents an integrated system for the breast cancer detection from mammograms based on automated mass detection, classification, and retrieval with a goal to support decision-making by retrieving and displaying the relevant past cases as well as predicting the images as benign or malignant. It is hypothesized that the proposed diagnostic aid would refresh the radiologist’s mental memory to guide them to a precise diagnosis with concrete visualizations instead of only suggesting a second diagnosis like many other CAD systems. Towards achieving this goal, a Graph-Based Visual Saliency (GBVS) method is used for automatic mass detection, invariant features are extracted based on using Non-Subsampled Contourlet transform (NSCT) and eigenvalues of the Hessian matrix in a histogram of oriented gradients (HOG), and finally classification and retrieval are performed based on using Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and a linear combination-based similarity fusion approach. The image retrieval and classification performances are evaluated and compared in the benchmark Digital Database for Screening Mammography (DDSM) of 2604 cases by using both the precision-recall and classification accuracies. Experimental results demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application

Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

A similarity study of contentâ based image retrieval system for breast cancer using decision tree

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134877/1/mp0277.pd

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists’ performance in finding and discriminat- ing between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detec- tion on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Fea- ture extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification tech- niques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed

Image analysis in medical imaging: recent advances in selected examples

Medical imaging has developed into one of the most important fields within scientific imaging due to the rapid and continuing progress in computerised medical image visualisation and advances in analysis methods and computer-aided diagnosis. Several research applications are selected to illustrate the advances in image analysis algorithms and visualisation. Recent results, including previously unpublished data, are presented to illustrate the challenges and ongoing developments

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