Three-Dimensional Spatio-Temporal Features for Fast Content-based Retrieval of Focal Liver Lesions

Content-based image retrieval (CBIR) systems for three-dimensional (3D) medical datasets still largely rely on twodimensional (2D) image-based features extracted from a few representative slices of the image stack. Most 2D features that are currently used in the literature not only model a 3D tumor incompletely but are also highly expensive in terms of computation time, especially for high resolution datasets. Radiologist-specified semantic labels are sometimes used along with image-based 2D features to improve the retrieval performance. Since radiological labels show large inter-user variability, are often un-structured, and require user interaction, their use as lesion characterizing features is highly subjective, tedious and slow. In this paper, we propose a 3D image-based spatio-temporal feature extraction framework for fast content-based retrieval of focal liver lesions. All the features are computer-generated and are extracted from 4-phase abdominal CT images. Retrieval performance and query processing times for the proposed framework is evaluated on a database of 44 hepatic lesions comprising of five pathological types. Bull’s eye percentage score above 85% is achieved for three out of the five lesion pathologies and for 98% of query lesions, at least one same type of lesion is ranked among the top two retrieved results. Experiments show that the proposed system’s query processing is more than 20 times faster than other already published systems that use 2D features. With fast computation time and high retrieval accuracy, the proposed system has the potential to be used as an assistant to radiologists for routine hepatic tumor diagnosis.