Content based medical image retrieval using topic and location model

Retrieval of medical images from an anatomically diverse dataset is a challenging task. Objective of our present study is to analyse the automated medical image retrieval system incorporating topic and location probabilities to enhance the performance.

In this paper, we present an automated medical image retrieval system using Topic and Location Model. The topic information is generated using Guided Latent Dirichlet Allocation (GuidedLDA) method. A novel Location Model is proposed to incorporate the spatial information of visual words. We also introduce a new metric called position weighted Precision (wPrecision) to measure the rank order of the retrieved images.

Experiments on two large medical image datasets - IRMA 2009 and Multimodal dataset - revealed that the proposed method outperforms existing medical image retrieval systems in terms of Precision and Mean Average Precision. The proposed method achieved better Mean Average Precision (86.74%) compared to the recent medical image retrieval systems using the Multimodal dataset with 7200 images. The proposed system achieved better Precision (97.5%) for top ten images compared to the recent medical image retrieval systems using IRMA 2009 dataset with 14,410 images.

Supplementing spatial details of visual words to the Topic Model enhances the retrieval efficiency of medical images from large repositories. Such automated medical image retrieval systems can be used to assist physician to retrieve medical images with better precision compared to the state-of-the-art retrieval systems.