基于内容的医学图像检索高维数据库系统的设计

Design of High-dimensional Database System for Medical Image Retrieval Based on Content

在医学影像信息学中,基于内容的图像检索(CBIR)技术被用来帮助放射科医师检索具有相似图像容的图像。CBIR通过图像的量化特征来检索高维特征数据库中与查询图像类似的影像。然而,当前大部分CBIR系统都会受特征维数影响,系统检索的响应时间随着特征向量维数及检索图像数量的增加而增长。对此提出了一种新的框架,使用VA-Trie结构来对图像特征建立高维数据库索引,以提高集成RIS/PACS中的CBIR的准确性和检索速度。

In medical imaging informatics, content-based image retrieval （CBIR） techniques are employed to aid radiologists in the retrieval of images with similar image contents. CBIR uses the quantization feature of the image to search for images similar to the query image in the high dimensional feature database. However, most of the current CBIR systems are affected by the feature dimension, and the response time of the system search increases with the increase of the feature vector dimertsion. In this presentation, we propose a novel framework to use the VA-Trie structure to establish high-dimensional database indexes for image features to improve the accuracy and retrieval speed of CBIR in integrated RIS / PACS.