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MEDICAL IMAGE RETRIEVAL METHOD BASED ON IMAGE CLUSTERING

机译:基于图像聚类的医学图像检索方法

摘要

The present invention relates to a medical image search method based on image clustering. The method includes: (a) a step of dividing each of a plurality of inputted medical images into sub areas and extracting an oriented center symmetric local binary patterns (OCS-LBP) feature value by patch unit; (b) a step of clustering the feature value into a code book and converting each image divided by patch unit into a bag-of-feature (BoF) through the code book; (c) a step of generating the BoF feature as a feature vector and learning and classifying the feature vector through a random forest learning classifier; (d) a step of repeating (a) through (c) when a query image is inputted and measuring the similarities with the medical image through a small number of specific classes among the classified classes; and (e) a step of outputting a search output image in accordance to the measured similarity. The present invention provides the medical image search method suggesting a new OCS-LBP feature suitable for a medical image to efficiently search for the medical image, applying BoF to efficiently reduce a feature dimension, and show a high performance search result by measuring a similarity distance through the random forest classifier to improve the search speed and performance instead of matching the query image with the entire image.
机译:基于图像聚类的医学图像搜索方法技术领域本发明涉及一种基于图像聚类的医学图像搜索方法。该方法包括:(a)将多个输入的医学图像中的每一个划分为子区域并以补丁单元提取定向的中心对称局部二进制图案(OCS-LBP)特征值的步骤; (b)将特征值聚类到代码簿中,并通过代码簿将按补丁单元划分的每个图像转换成特征包(BoF)的步骤; (c)生成BoF特征作为特征向量并通过随机森林学习分类器对特征向量进行学习和分类的步骤; (d)当输入查询图像时重复(a)至(c)并通过分类的类别中的少数特定类别来测量与医学图像的相似度的步骤; (e)根据测得的相似度输出搜索输出图像的步骤。本发明提供一种医学图像搜索方法,该医学图像搜索方法建议适用于医学图像的新的OCS-LBP特征以有效地搜索医学图像,应用BoF以有效地减小特征尺寸,并通过测量相似距离来显示高性能搜索结果。通过随机森林分类器来提高搜索速度和性能,而不是将查询图像与整个图像进行匹配。

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