Grayscale Radiograph Annotation Using Local Relational Features

摘要

Content based automatic image classification systems are increasingly finding usage, e.g. in large medical image databases. This paper concentrates on a grayscale radiograph annotation task which was a part of the ImageCLEF 2006. We use local features calculated around interest points, which have recently received excellent results for various image recognition and classification tasks. We propose the use of relational features, which are highly robust to illumination changes, and thus quite suitable for X-Ray images. Results with various feature and classifier settings are reported. A significant improvement in results is seen when the relative positions of the interest points are also taken into account during matching. For the given test set, our best run had a classification error rate of 16.7 %, just 0.5 % higher than the best overall submission, and therewith was ranked second in the medical automatic annotation task at the ImageCLEF 2006. The proposed method is general, can be applied to other image classification tasks and can also be extended to colour images.