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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的人视网膜脉管系统分析

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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,已经尝试分析基于内容的图像检索(CBIR)应用的视网膜图像。基于Canny Edge的CBIR系统是在没有预处理技术的情况下开发的。使用罐头边缘法分割正常和异常视网膜图像的血管。基于结构和纹理的特征是从分段图像获得的。相似性比较使用Bhattacharyya距离测量进行。检索到的图像排列。基于它们的性能措施,如精度和召回,比较了CBIR系统的检索效率。结果表明,使用具有形态学预处理的罐头衍生的特征可以显着区分正常和异常的视网膜图像。使用具有预处理的CBIR系统的CBIR系统的精度和调用比没有预处理更好。似乎这个CBIR系统辅助视网膜异常的诊断。

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