使用加权规则的乳腺X光图像关联分类
Associative Classification of Mammograms using Weighted Rules.
作者信息
Dua Sumeet, Singh Harpreet, Thompson H W
机构信息
Department of Computer Science, Department of Computer Science, Louisiana Tech University, P.O. Box 10348, Ruston, LA 71270 and with the School of Medicine, LSU Health Sciences Center, 2020 Gravier Street, New Orleans, LA 70112. (; fax: 318-257-4922; e-mail:
出版信息
Expert Syst Appl. 2009 Jul 1;36(5):9250-9259. doi: 10.1016/j.eswa.2008.12.050.
In this paper, we present a novel method for the classification of mammograms using a unique weighted association rule based classifier. Images are preprocessed to reveal regions of interest. Texture components are extracted from segmented parts of the image and discretized for rule discovery. Association rules are derived between various texture components extracted from segments of images, and employed for classification based on their intra- and inter-class dependencies. These rules are then employed for the classification of a commonly used mammography dataset, and rigorous experimentation is performed to evaluate the rules' efficacy under different classification scenarios. The experimental results show that this method works well for such datasets, incurring accuracies as high as 89%, which surpasses the accuracy rates of other rule based classification techniques.
在本文中，我们提出了一种新颖的方法，用于使用基于独特加权关联规则的分类器对乳房X光图像进行分类。对图像进行预处理以揭示感兴趣区域。从图像的分割部分提取纹理成分并进行离散化以发现规则。在从图像片段中提取的各种纹理成分之间推导关联规则，并根据它们的类内和类间依赖性用于分类。然后将这些规则用于对常用的乳房X光摄影数据集进行分类，并进行严格的实验以评估规则在不同分类场景下的有效性。实验结果表明，该方法适用于此类数据集，准确率高达89%，超过了其他基于规则的分类技术的准确率。
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