Clinical Content Detection for Medical Image Retrieval

Content-based image retrieval (CBIR) is the most widely used method for searching large-scale medical image collections; however this approach is not suitable for high-level applications as human experts are accustomed to manage medical images based on their clinical features rather than primitive features. Automatic detection of clinical features in a large-scale image database and realization of image retrieval by clinical content are still open issues. This paper presents a Markov random field (MRF) based model for clinical content detection. Multiple classifiers are applied to recognize a wide range of clinical features in a large-scale histological image database, and they are further combined to generate more reliable and robust estimation. Spatial contexts will cooperate with local estimations in the MRF based model to make a decision based on global consistency. The detected clinical features will provide a basis for image retrieval. Experiments have been carried out in a large-scale histological image database with promising results