Content-based Medical Image Tetrieval for Liver CT Annotation

Abstract

The increase number of medical image stored and saved every day presents a unique opportunity for content-based medical image retrieval (CBMIR) systems. In this paper, we propose content-based medical image retrieval for annotating liver CT scans images in order to generate a structured report. For that, we have used the Bidimentional Empirical Mode Decomposition (BEMD), and then we have applied Gabor wavelet transform to extract the mean and the standard deviation as features descriptors. Finally, a proposed similarity distance was employed to retrieve the most similar training images to the image query, and a majority voting scheme was used to select the answers for an unannotated image. We have used the IMAGECLEF 2015 annotation dataset and the obtained score was 88.9%.