首页> 外文会议>International Workshop on Digital Mammography(IWDM 2006); 20060618-21; Manchester(GB) >Calcification Descriptor and Relevance Feedback Learning Algorithms for Content-Based Mammogram Retrieval
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Calcification Descriptor and Relevance Feedback Learning Algorithms for Content-Based Mammogram Retrieval

机译:基于内容的乳房X线照片检索的钙化描述符和相关反馈学习算法

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摘要

In recent years a large number of digital mammograms have been generated in hospitals and breast screening centers. To assist diagnosis through indexing those mammogram databases, we proposed a content-based image retrieval framework along with a novel feature extraction technique for describing the degree of calcification phenomenon revealed in the mammograms and six relevance feedback learning algorithms, which fall in the category of query point movement, for improving system performance. The results show that the proposed system can reach a precision rate of 0.716 after five rounds of relevance feedback have been performed.
机译:近年来,在医院和乳房筛查中心已经产生了大量的数字化乳房X线照片。为了通过对这些乳房X线照片数据库建立索引来辅助诊断,我们提出了一种基于内容的图像检索框架,以及一种新颖的特征提取技术,用于描述乳房X线照片中揭示的钙化现象的程度以及六种相关反馈学习算法,它们属于查询类别点运动,以提高系统性能。结果表明,提出的系统经过五轮相关反馈后,可以达到0.716的精度。

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