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首页> 外文期刊>Journal of Digital Imaging >A Comprehensive Descriptor of Shape: Method and Application to Content-Based Retrieval of Similar Appearing Lesions in Medical Images
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A Comprehensive Descriptor of Shape: Method and Application to Content-Based Retrieval of Similar Appearing Lesions in Medical Images

机译：形状的全面描述：医学图像中相似出现病变的基于内容的检索方法及其应用

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摘要

We have developed a method to quantify the shape of liver lesions in CT images and to evaluate its performance for retrieval of images with similarly-shaped lesions. We employed a machine learning method to combine several shape descriptors and defined similarity measures for a pair of shapes as a weighted combination of distances calculated based on each feature. We created a dataset of 144 simulated shapes and established several reference standards for similarity and computed the optimal weights so that the retrieval result agrees best with the reference standard. Then we evaluated our method on a clinical database consisting of 79 portal-venous-phase CT liver images, where we derived a reference standard of similarity from radiologists’ visual evaluation. Normalized Discounted Cumulative Gain (NDCG) was calculated to compare this ordering with the expected ordering based on the reference standard. For the simulated lesions, the mean NDCG values ranged from 91% to 100%, indicating that our methods for combining features were very accurate in representing true similarity. For the clinical images, the mean NDCG values were still around 90%, suggesting a strong correlation between the computed similarity and the independent similarity reference derived the radiologists.
机译：我们已经开发出一种方法，可以量化CT图像中肝脏病变的形状，并评估其在检索形状相似的病变图像时的性能。我们采用了一种机器学习方法来组合多个形状描述符，并为一对形状定义相似性度量，作为基于每个特征计算的距离的加权组合。我们创建了144个模拟形状的数据集，并为相似性建立了多个参考标准，并计算了最佳权重，以便检索结果与参考标准最吻合。然后，我们在由79张门静脉期CT肝脏图像组成的临床数据库中评估了我们的方法，并从放射科医生的视觉评估中得出了相似性的参考标准。计算归一化贴现累积增益（NDCG），以将该排序与基于参考标准的预期排序进行比较。对于模拟病变，平均NDCG值范围为91％至100％，这表明我们的特征组合方法在表示真实相似性方面非常准确。对于临床图像，平均NDCG值仍在90％左右，这表明计算出的相似性与放射科医生得出的独立相似性参考之间存在很强的相关性。

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