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MIRACLE at ImageCLEFannot 2008: Nearest Neighbour Classification of Image Feature Vectors for Medical Image Annotation

机译:ImageClefannot 2008的奇迹:最近的图像特征向量的邻居分类用于医学图像注释

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This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2008. During the last year, our own image analysis system was developed, based on MATLAB. This system extracts a variety of global and local features including histogram, image statistics, Gabor features, fractal dimension, DCT and DWT coefficients, Tamura features and co-occurrence matrix statistics. A classifier based on the k-Nearest Neighbour algorithm is trained on the extracted image feature vectors to determine the IRMA code associated to a given image, The focus of our participation was mainly to test and evaluate this system in-depth and to compare among diverse configuration parameters such as number of images for the relevance feedback to use in the classification module.
机译:本文介绍了奇迹研究联盟在ImageClef 2008的ImageClef Medical Image Annotation任务中的参与。在去年,我们自己的图像分析系统是基于Matlab的。该系统提取各种全局和本地特征,包括直方图,图像统计,GABOR功能,分形维数,DCT和DWT系数,Tamura特征和共同发生矩阵统计。基于k最近邻算法的分类器在提取的图像特征向量上培训,以确定与给定图像相关联的IRMA代码,我们参与的焦点主要是测试和评估该系统深入并比较多样化配置参数,例如用于分类模块的相关反馈的图像数量。

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