Three-Dimensional Spatiotemporal Features for Fast Content-Based Retrieval of Focal Liver Lesions

Content-based image retrieval systems for $\hbox{3}$ -D medical datasets still largely rely on $\hbox{2}$ -D image-based features extracted from a few representative slices of the image stack. Most $\hbox{2}$ -D features that are currently used in the literature not only model a $\hbox{3}$ -D 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 $\hbox{2}$ -D features to improve the retrieval performance. Since radiological labels show large interuser variability, are often unstructured, and require user interaction, their use as lesion characterizing features is highly subjective, tedious, and slow. In this paper, we propose a $\hbox{3}$ -D image-based spatiotemporal feature extraction framework for fast content-based retrieval of focal liver lesions. All the features are computer generated and are extracted from four-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 $2$ -D 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.