Medical Image Retrieval and Automatic Annotation: OHSU at ImageCLEF 2007

Oregon Health & Science University participated in the medical retrieval and medical annotation tasks of ImageCLEF 2007. In the medical retrieval task, we created a web-based retrieval system built on a full-text index of both image and case annotations. The text-based search engine was implemented in Ruby using Ferret, a port of Lucene and a custom query parser. In addition to this textual index of annotations, supervised machine learning techniques using visual features were used to classify the images based on image acquisition modality. All images were annotated with the purported modality. Purely textual runs as well as mixed runs using the purported modality were submitted, with the latter performing among the best of all participating research groups. In the automatic annotation task, we used the 'gist' technique to create the feature vectors. Using statistics derived from a set of multi-scale oriented filters, we created a 512-dimensional vector. PCA was then used to create a 100-dimensional vector. This feature vector was fed into a two layer neural network. Our error rate on the 1000 test images was 67.8 using the hierarchical error calculations. 1. Medical Image Retrieval Advances in digital imaging technologies and the increasing prevalence of Picture Archival and Communication Systems (PACS) have led to a substantial growth in the number of digital images stored in hospitals and medical systems in recent years. In addition, on-line atlases of images have been created for many medical domains including dermatology, radiology and gastroenterology. Medical images can form an essential component of a patient’s health record. Medical image retrieval systems can be important with aiding in diagnosis and treatment. They can also be highly effective in health care education, for students, instructors and patients.