论文标题
一袋医学图像检索的视觉单词模型
A Bag of Visual Words Model for Medical Image Retrieval
论文作者
论文摘要
由于基础内容的多维和多模式上下文，医疗图像检索是视觉信息检索的挑战性领域。传统模型通常无法考虑数据的内在特征，因此将其应用于医学图像时的准确性有限。视觉单词袋（BOVW）是一种可用于有效代表向量空间中固有图像特征的技术，因此可以优化图像分类和类似图像搜索之类的应用程序。在本文中，我们提出了一种基于BOVW模型的MEDIR方法，用于基于内容的医学图像检索。作为多维的医学图像，它们表现出潜在的群集和歧管信息，从而增强了语义相关性并允许标签均匀性。因此，为每个图像提取的BOVW功能用于基于正面和负面培训图像的监督机器学习分类器，以扩展基于内容的图像检索。在实验验证期间，提出的模型表现良好，在前3个图像检索实验中，平均平均精度为88.89％。
Medical Image Retrieval is a challenging field in Visual information retrieval, due to the multi-dimensional and multi-modal context of the underlying content. Traditional models often fail to take the intrinsic characteristics of data into consideration, and have thus achieved limited accuracy when applied to medical images. The Bag of Visual Words (BoVW) is a technique that can be used to effectively represent intrinsic image features in vector space, so that applications like image classification and similar-image search can be optimized. In this paper, we present a MedIR approach based on the BoVW model for content-based medical image retrieval. As medical images as multi-dimensional, they exhibit underlying cluster and manifold information which enhances semantic relevance and allows for label uniformity. Hence, the BoVW features extracted for each image are used to train a supervised machine learning classifier based on positive and negative training images, for extending content based image retrieval. During experimental validation, the proposed model performed very well, achieving a Mean Average Precision of 88.89% during top-3 image retrieval experiments.
