Rahman Md Mahmudur, Desai Bipin C, Bhattacharya Prabir
Department of Computer Science & Software Engineering, Concordia University, Montreal, Canada.
Comput Med Imaging Graph. 2008 Mar;32(2):95-108. doi: 10.1016/j.compmedimag.2007.10.001. Epub 2007 Nov 26.
We present a content-based image retrieval framework for diverse collections of medical images of different modalities, anatomical regions, acquisition views, and biological systems. For the image representation, the probabilistic output from multi-class support vector machines (SVMs) with low-level features as inputs are represented as a vector of confidence or membership scores of pre-defined image categories. The outputs are combined for feature-level fusion and retrieval based on the combination rules that are derived by following Bayes' theorem. We also propose an adaptive similarity fusion approach based on a linear combination of individual feature level similarities. The feature weights are calculated by considering both the precision and the rank order information of top retrieved relevant images as predicted by SVMs. The weights are dynamically updated by the system for each individual search to produce effective results. The experiments and analysis of the results are based on a diverse medical image collection of 11,000 images of 116 categories. The performances of the classification and retrieval algorithms are evaluated both in terms of error rate and precision-recall. Our results demonstrate the effectiveness of the proposed framework as compared to the commonly used approaches based on low-level feature descriptors.
我们提出了一种基于内容的图像检索框架，用于处理不同模态、解剖区域、采集视图和生物系统的各种医学图像集合。对于图像表示，以低级特征作为输入的多类支持向量机（SVM）的概率输出被表示为预定义图像类别的置信度或隶属度得分向量。基于遵循贝叶斯定理推导的组合规则，将输出进行组合以进行特征级融合和检索。我们还提出了一种基于个体特征级相似度线性组合的自适应相似度融合方法。通过考虑SVM预测的顶级检索相关图像的精度和排名顺序信息来计算特征权重。系统会为每个单独的搜索动态更新权重，以产生有效的结果。实验和结果分析基于一个包含116个类别的11000张图像的多样化医学图像集合。分类和检索算法的性能通过错误率和精确率-召回率进行评估。我们的结果表明，与基于低级特征描述符的常用方法相比，所提出的框架是有效的。
