MedInject: a general-purpose information retrieval framework applied in a medical context

The continuous improvement of medical software and instrumentation have contributed to generate large amounts of medical image data. Thus, plenty of Content-Based Image Retrieval systems have emerged in order to index and retrieve images according to similarity criteria. Some of those systems are applied in very specific domains, such as mammography, lung or spine exams. Others, however, are general-purpose applications that can be adopted in a medical environment. In such context, we realized those specific systems could benefit from the facilities brought by generic frameworks and propose our solution. This article presents a novel information retrieval core framework that performs both indexing and similarity search operations over medical image data sets. The framework follows a modular architecture based on Design Patterns and can be easily extended, allowing to other system developers to take advantages of its functions by using the provided interfaces. We performed extensive experiments evaluating several of its properties and target abstractions using medical real data, and show that it allows the implementation to achieve proper similarity retrieval and significant performance improvements in relation to the existing alternatives.CAPESCNPQFAPEMIGFAPES