Semantic Annotation and Classification of Mammography Images using Ontologies

Abstract

Breast cancer is one of the most common types of cancer among women in the world. The early diagnosis of the anomaly is a priority for more effective and less costly treatment. Mammography is the most used medical exam in diagnosing breast cancer, and the number of images generated grows exponentially. Accurately interpreting and diagnosing mammography images requires that the specialist has extensive experience to perform this task. The manual image classification and the retrieval of similar cases is an expensive and time-consuming process. The historical data in these exams are an excellent source of knowledge and should be considered when analyzing new cases. Thereby, this research aims to improve the classification and semantic annotation of medical images. We propose a method to extract the low-level image features, metadata information, and medical reports and combine them with the high-level information present in a domain ontology. Also, providing an appropriate semantic structure for integrating information enables the classification and semantic image annotation through a reasoner. Thus, this work seeks to facilitate radiologists' and physicians' daily lives involved in breast cancer diagnosis, collaborating with a second opinion for the medical diagnosis. The experiments have shown that the proposed method is feasible and can generate promising results, achieving a 71% sensitivity rate in identifying circumscribed findings. Moreover, it favors classification and diagnosis as it allows semantically annotating images with concepts of multiple abstraction levels. Therefore, semantic annotation reduces the semantic gap and can help to improve CAD and CBIR systems. The semantic annotation also collaborates with medical education to support the teaching and training of reading medical images and medical diagnosis, supporting decision making by providing a second opinion.