An Efficient Medical Content Based Image Retrieval with Lenient Relevance Feedback

Content Based Image Retrieval (CBIR) for medical imageries is still in its early stage.  There are many challenging research issues.  Retrieve similar images only is the current problem in medical CBIR. One idea to solve this difficult is minimizing the gap among two descriptions i.e. low level extracted features of image and high level human perception of image.  There are various Relevance Feedback (WF) methods have been considered to minimize the semantic gap in medical CBIR system. But most of them were deals with hard Feedback. In Hard Feedback system user can interact with the system in one query session. We recommend to aid the usage of lenient relevance response to better capture the intention of users. The meta-knowledge mined from multiple user’s experience be able to   increase the precision of subsequent image recovery results. Here we suggest an algorithm to mine lenient association rules from the group of suggestion i.e. image weight value given by the user. To reduce the amount of strong rules we offer two rule lessening techniques related to redundancy detection and confidence quantization.  Best first search and Binary search methods are similarly applied to advance the procedure of weight interface. The effectiveness of the offered system is assessed regarding precision and average retrieval time. The experimental results on medical images display that the proposed method is able to improve the accuracy of medical CBIR system and reduces the retrieval time than other usual methods.