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Analysis of Human Retinal Vasculature for Content Based Image Retrieval Applications

机译:基于内容的图像检索应用中的人体视网膜血管分析

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In this work, an attempt has been made to analyse retinal images for Content Based Image Retrieval (CBIR) application. Canny edge based CBIR systems are developed with and without preprocessing techniques. Blood vessels of normal and abnormal retinal images are segmented using Canny edge method. The structural and texture based features are obtained from segmented images. Similarity comparison is carried out using Bhattacharyya distance measure. The retrieved images are ranked. Retrieval efficiency of the CBIR systems is compared based on their performance measures such as precision and recall. The results demonstrate that features derived using Canny with morphological preprocessing could differentiate normal and abnormal retinal images significantly. Precision and recall of the CBIR system using Canny with preprocessing is found to be better than without preprocessing. It appears that this CBIR system aids in diagnosis of retinal abnormalities.
机译:在这项工作中,已经尝试分析视网膜图像以用于基于内容的图像检索(CBIR)应用程序。基于Canny edge的CBIR系统是在有或没有预处理技术的情况下开发的。正常和异常视网膜图像的血管使用Canny边缘法进行分割。从分割的图像获得基于结构和纹理的特征。使用Bhattacharyya距离度量进行相似度比较。对检索到的图像进行排名。根据CBIR系统的性能指标(如精度和召回率)比较检索效率。结果表明,使用Canny进行形态学预处理后得到的特征可以显着区分正常和异常的视网膜图像。发现使用Canny进行预处理的CBIR系统的精度和召回率比不进行预处理的更好。看来,这种CBIR系统有助于视网膜异常的诊断。

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