It is now recognized in many domains that content-based image retrieval (CBIR) from a database of images cannot be carried out by using completely automated approaches. One such domain is medical radiology for which the clinically useful information in an image typically consists of gray level variations in highly localized regions of the image. Currently, it is not possible to extract these regions by automatic image segmentation techniques. To address this problem, we have implemented a human-in-the-loop (a physician-in-the-loop, more speci cally) approach in which the human delineates the pathology bearing regions (PBR) and a set of anatomical landmarks in the image when the image is entered into the database. From the regions thus marked, our approach applies low-level computer vision and image processing algorithms to extract attributes related to the variations in gray scale, texture, shape, etc. In addition, the system records attributes that capture relational information such as the position of a PBR with respect to certain anatomical landmarks. An overall multi-dimensional index is assigned to each image based on these attribute values.
Shyu et al. REF describe a human-in-the-loop approach where the human annotator delineates the region of interest and anatomical landmarks in the images, followed by classification on image attributes related to variations in intensity, texture, shape descriptors and so on.
ASSERT: A Physician-in-the-loop Content-Based Retrieval System for HRCT Image Databases
Comput. Vis. Image Underst.
Comput. Vis. Image Underst.
Computer Science Mathematics