An integrated scattering feature with application to medical image retrieval

Scattering transform has been successfully applied to medical image retrieval because it provides extensive semantic representations of an image. To efficiently handle the scattering transform results, the coefficient matrices are usually compressed to vectors. However, the existing features derived from the compressed vectors only consider one distribution information of the original image. To address this problem, this paper proposes an integrated scattering feature for medical image retrieval. The proposed method integrates two types of compressed scattering data from different perspectives, namely data concentration and canonical correlation analysis (CCA). For each integration model, we also give a corresponding feature representation strategy that takes account of more comprehensive characteristics of original medical image. Experiments on two benchmark medical computed tomography (CT) image databases demonstrate the superiorities of the proposed features over several state-of-the-art methods.