Medical Image Retrieval based on LBP Histogram Fourier features and KNN classifier

P. Bharathi, K. Reddy, G. Srilakshmi
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引用次数: 8

Abstract

In the recent past, Content Based Image Retrieval Systems has become much important in various applications such as medical images. Such systems must effectively retrieve medical images of the same kind. In this paper, we propose to extract local binary pattern (LBP) Histogram Fourier features from each image and the query image, perform the histogram intersection and then apply KNN classifier over the output images of the histogram intersection such that only nearby distance images with respect to the query image is retrieved, thus improving the performance of the system in terms of precision and recall. The proposed technique is compared with that of the histogram intersection based classification and KNN classification separately. Experimental results demonstrate better accuracy of the proposed technique when compared to the existing techniques.
基于LBP直方图傅立叶特征和KNN分类器的医学图像检索
近年来,基于内容的图像检索系统在诸如医学图像等各种应用中变得越来越重要。这样的系统必须有效地检索同类的医学图像。在本文中,我们提出从每个图像和查询图像中提取局部二值模式(LBP)直方图傅里叶特征,进行直方图相交,然后在直方图相交的输出图像上应用KNN分类器,以便仅检索相对于查询图像的近距离图像,从而提高系统在精度和查全率方面的性能。将该方法分别与基于直方图交集的分类方法和KNN分类方法进行了比较。实验结果表明,与现有技术相比,该技术具有更高的精度。
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