结合实值和二值Gabor-Radon特征的医学影像档案分类检索
Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives
摘要：
基于内容的图像检索（CBIR）的医学图像在大型数据集，以识别相似的图像时，查询图像是非常有用的，在提高诊断决策的临床专家，以及在教育场景。在本文中，我们使用两个阶段的分类和检索方法来检索相似的图像。首先，应用Gabor滤波器对Radon变换后的图像进行特征提取，并训练多类支持向量机。然后基于分类结果，使用提取的Gabor条形码，检索相似的图像。该方法在包含14，000多张图像的IRMA数据集上进行了测试。实验结果表明，我们的方法在检索相似的图像相比，其他Gabor-Radon面向方法的效率。
英文摘要：
Content-based image retrieval (CBIR) of medical images in large datasets to identify similar images when a query image is given can be very useful in improving the diagnostic decision of the clinical experts and as well in educational scenarios. In this paper, we used two stage classification and retrieval approach to retrieve similar images. First, the Gabor filters are applied to Radon-transformed images to extract features and to train a multi-class SVM. Then based on the classification results and using an extracted Gabor barcode, similar images are retrieved. The proposed method was tested on IRMA dataset which contains more than 14,000 images. Experimental results show the efficiency of our approach in retrieving similar images compared to other Gabor-Radon-oriented methods.
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