Reweighting BiasMap Based Image Retrieval and Relevance Feedback for Medical Cerebral MRI Image

摘要

This paper proposed a region based image retrieval and relevant feedback (RF) system for Medical cerebral MRI images. In the system, firstly, the brains were extracted from cerebral images by a modified BET algorithm, and then were segmented into regions by EM algorithm based on Gauss Mixture Model. Each region was represented by fuzzy features. When performing retrieval, both regional and global features were used. To optimize the retrieval result, this paper used reweighting relevance feedback method (RW) to optimize regional features and proposed reweighting BiasMap based relevance feedback method (RW-BiasMap) to optimize global features. The computation of RW is very fast, but only uses the relevant images. RW-BiasMap is based on RW and BiasMap feedback method, it can use both the relevant images and the irrelevant images, but the computation of RW-BiasMap is slowly, so this paper only uses it to optimize the global features. Experiments show that this retrieval system is effective and RW-BiasMap performs better than BiasMap.