A Tensor Sparse Representation-Based CBMIR System for Computer-Aided Diagnosis of Focal Liver Lesions and its Pilot Trial

Clinicians refer to diagnosed medical cases in order to make correct diagnosis and take appropriate treatments, due to the complexity of focal liver lesions. It's a heavy burden, however, for medical doctors to find out similar and meaningful cases from the accumulated extreme large medical datasets. Content based medical image retrieval (CBMIR) that searches for similar images in a large database has been attracting increasing research interest recently. A CBMIR system provides doctors the diagnosed cases to improve the diagnosis accuracy and confidence. This paper proposed a tensor sparse representation method to extract temporal and spatial features of multi-phase CT images, so as to provide doctors medical cases more relevant to the query one. The proposed tensor sparse representation method is applied to the retrieval of focal liver lesions (FLLs). Experiments show that the proposed method achieved better retrieval performance than conventional methods. Pilot trial was conducted and results show that diagnosis accuracy and confidence was improved significantly by the developed CBMIR system based on the proposed method.