Brain CT image database building for computer-aided diagnosis using content-based image retrieval

Content-based image retrieval for medical images is a primary technique for computer-aided diagnosis. While it is a premise for computer-aided diagnosis system to build an efficient medical image database which is paid less attention than that it deserves. In this paper, we provide an efficient approach to develop the archives of large brain CT medical data. Medical images are securely acquired along with relevant diagnosis reports and then cleansed, validated and enhanced. Then some sophisticated image processing algorithms including image normalization and registration are applied to make sure that only corresponding anatomy regions could be compared in image matching. A vector of features is extracted by non-negative tensor factorization and associated with each image, which is essential for the content-based image retrieval. Our experiments prove the efficiency and promising prospect of this database building method for computer-aided diagnosis system. The brain CT image database we built could provide radiologists with a convenient access to retrieve pre-diagnosed, validated and highly relevant examples based on image content and obtain computer-aided diagnosis.