Shindang-Dong Dalseo-Gu, Department Of Computer Engineering, Keimyung University, Daegu, 704-701, South Korea.
J Digit Imaging. 2012 Aug;25(4):454-65. doi: 10.1007/s10278-011-9443-5.
This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.
本文提出了一种新颖的医学图像多关键词标注方法、基于关键词的医学图像检索以及用于图像检索的相关反馈方法，以提高图像检索性能。在语义关键词标注方面，本研究提出了一种新的基于局部小波的中心对称局部二值模式与随机森林相结合的医学图像分类方法。在基于关键词的图像检索方面，我们的检索系统使用置信度评分，该评分通过结合随机森林的概率和预定义的身体关系图分配给每个标注的关键词。为了克服基于关键词的图像检索的局限性，我们将我们的图像检索系统与基于视觉特征和模式分类器的相关反馈机制相结合。与其他标注和相关反馈算法相比，所提出的方法在标注性能和检索结果的准确性方面都有所提高。
