Spatial lesion indexing for medical image databases using force histograms

It is often difficult to find a well-principled approach for the selection of a spatial indexing mechanism for medical image databases. Spatial information concerning lesions in medical images is critically important in disease diagnosis and plays an important role in image retrieval. Unfortunately, images are rarely indexed properly for clinically useful retrieval. One example is the well-known R-tree and its variants which index image objects based on their physical locations in an "absolute" way. However, such information is not meaningful in medical content-based image retrieval systems, and the approaches suffer from problems caused by variations in object size and shape, imprecise image centering, etc. A more appropriate approach, which does not require object registration, is to model the spatial relationships between lesions and anatomical landmarks. To convey diagnostic information, lesions must exist in certain locations with regard to landmarks. In this paper, we show that the histogram of forces (which represents the relative position between two objects) provides an efficient spatial indexing mechanism in the medical domain.