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首页> 外文期刊>Journal of digital information management >Medical Image Retrieval Based on An Improved Non-negative Matrix Factorization Algorithm
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Medical Image Retrieval Based on An Improved Non-negative Matrix Factorization Algorithm

机译：基于改进的非负矩阵分解算法的医学图像检索

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摘要

A gradient-projected relevance feedback algorithm based on non-negative matrix factorization (NMF) is proposed in this study to improve the performance of retrieval algorithm in the medical image processing field. Relevance feedback has been an important method in image retrieval technology in recent years because it allows users to participate. Thus, it can compensate for the shortcomings of using low-level features to describe the semantic contents of an image to some degree. Given that NMF can partly sketch the distribution of relevant images in the space represented by the base matrix, finding more related images from image repositories is possible. This condition can be achieved by conducting an NMF operation of the query image, using the gradient projection iterative rules to update variables, and selecting the appropriate iteration stop conditions to optimize the time complexity of the algorithm. Compared with the commonly used and multiplicative updating NMF approaches, the proposed method improved the speed of the feedback on the premise of guaranteeing precision and recall rates, and significantly optimized the retrieval accuracy. Experiments were conducted on the base of 586 cerebral hemorrhage images and 634 spine and cervical-spine mixed images. Results show that the proposed approach is feasible in medical image retrieval.
机译：提出了一种基于非负矩阵分解的梯度投影相关反馈算法，以提高检索算法在医学图像处理领域的性能。近年来，相关性反馈已经成为图像检索技术中的一种重要方法，因为它可以使用户参与其中。因此，它可以弥补使用低级特征在某种程度上描述图像语义内容的缺点。假设NMF可以部分勾勒出基本矩阵表示的空间中相关图像的分布，则可以从图像存储库中找到更多相关图像。可以通过对查询图像进行NMF操作，使用梯度投影迭代规则来更新变量以及选择适当的迭代停止条件以优化算法的时间复杂度来实现此条件。与常用的乘性更新NMF方法相比，该方法在保证精度和查全率的前提下，提高了反馈速度，极大地优化了检索精度。实验基于586例脑出血图像和634例脊柱和颈椎混合图像进行。结果表明，该方法在医学图像检索中是可行的。

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