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The Robust Algorithm of 3D Medical Image Retrieval Based on Perceptual Hashing

机译:基于感知散列的3D医学图像检索的鲁棒算法

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In order to deal with the difficulties of the heavy workloads and ambiguity of text labeling in the traditional image retrieval and the problem that the studies on 3D medical images retrieval were lacking, this paper proposes an robust algorithm based on perceptual hashing for 3D medical images retrieval. At first, the hash value of 3D medical image to be retrieved is extracted through perceptual hashing. And then the hash value database is established. Next, the NC(Normalized Cross Correlation Coefficient, NC) between the hash value of the image to be retrieved and each one in the hash value database is computed automatically. Finally the corresponding image with the highest NC value is retrieved and the 3D medical image retrieval is realized. The results show that this algorithm can distinguish different 3D images remarkably and has ideal robustness against Guassian noise, JPEG compression, Median filtering attacks, which is obviously better than other algorithms based on DCT, DFT. In addition, this algorithm has rapid retrieval capability and good practicability.
机译:为了应对传统图像检索中缺乏文本标签的繁重工作量和歧义的困难以及缺乏3D医学图像检索的研究的问题,本文提出了一种基于3D医学图像检索的感知散列的鲁棒算法。首先,通过感知散列提取要检索的3D医学图像的散列值。然后建立哈希值数据库。接下来,自动计算要检索的图像的散列值之间的散列值与散列值数据库中的每个NC(归一化交叉相关系数,NC)。最后,检索具有最高NC值的相应图像,并实现3D医学图像检索。结果表明,该算法可以显着区分不同的3D图像,具有对涉及鸟类噪声,JPEG压缩,中值过滤攻击的理想鲁棒性,这显然比基于DCT,DFT的其他算法更好。此外,该算法具有快速的检索能力和良好的实用性。

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