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Medical Image Retrieval Based on Gray Cluster Co-occurrence Matrix and Edge Strength Levels

机译:基于灰色聚类共生矩阵和边缘强度水平的医学图像检索

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Retrieving images depend of specific features In Content Based Image Retrieval (CBIR). A common approach is to divide retrieval process into two stages; the first one is based on high-level features followed by the second that is based on low-level features. We focus primarily on medical images, and follow the above approach but make the following two basic contributions: a) introduce the gray cluster co-occurrence matrix as texture feature extraction and use it as high-level features, and b) introduce edge strength levels as shape feature extraction and use it as low-level features. Our proposed system suggests the precision rate was 94.90% and recall rate was 89.72%. The distance variance achieved lowest rate (0.0022) in images retrieval compared to each of partial systems individually and related works. Our method has better performance in retrieving the results than other related works and each of partial system individually.
机译:检索图像取决于基于内容的图像检索(CBIR)中的特定功能。一种常见的方法是将检索过程分为两个阶段。第一个基于高级功能,第二个基于低级功能。我们主要关注医学图像,并遵循上述方法,但做出了以下两个基本贡献:a)引入灰色聚类共现矩阵作为纹理特征提取并将其用作高级特征,b)引入边缘强度等级作为形状特征提取并将其用作低级特征。我们提出的系统建议准确率为94.90%,召回率为89.72%。与部分局部系统和相关工作相比,距离方差在图像检索中达到最低比率(0.0022)。与其他相关工作和每个局部系统相比,我们的方法在检索结果方面具有更好的性能。

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