Superpixel-Based interest points for effective bags of visual words medical image retrieval

The present work introduces a 2D medical image retrieval system which employs interest points derived from superpixels in a bags of visual words (BVW) framework. BVWs rely on stable interest points so that the local descriptors can be clustered into representative, discriminative prototypes (the visual words). We show that using the centers of mass of superpixels as interest points yields higher retrieval accuracy when compared to using Difference of Gaussians (DoG) or a dense grid of interest points. Evaluation is performed on two data sets. The ImageCLEF 2009 data set of 14.400 radiographs is used in a categorization setting and the results compare favorable to more specialized methods. The second set contains 13 thorax CTs and is used in a hybrid 2D/3D localization task, localizing the axial position of the lung through the retrieval of representative 2D slices.