Medical Image Retrieval Based on Latent Semantic Indexing

To improve the performance of content-based medical image retrieval, herein an algorithm which makes use of latent semantic indexing (LSI) technology on gastroscopic image retrieval is proposed. First extract imagepsilas color histogram and color autocorrelogram of low-level features, and then use normalizing, term weighting and singular value decomposition to realize low-level features mapping into high-level semantic features. In this way, the retrieval results will be more in accordance with the query imagepsilas semantic content. Based on above idea, a prototype system which supports query by example image is designed and implemented. The experimental results according to the prototype system show that the approach proposed in the paper is effective to gastroscopic image retrieval.