...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert systems with applications >Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms
【24h】

Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms

机译：纹理提取：对基于乳房小波，小波和共现的方法进行乳房X线照片评估

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-cau-dal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC - 0.90.
机译：图像处理算法可用于计算机辅助诊断系统，以直接从数字化乳腺X线照片中提取特征。通常，使用这些算法从乳房X线照片中提取两类特征，即形态特征和非形态特征。图像纹理分析是一种重要的技术，代表了用于描述非形态特征的图像灰度级特性。该技术已被证明是分析由肿块引起的乳房X线照片病变的有前途的技术。在本文中，我们使用来自乳腺X射线摄影图像的共现矩阵，小波和脊波变换的特征来评估纹理分类。特别是，我们提出了在计算机辅助质量检测中的假阳性减少。数据集由120例颅底乳房X线照片组成，一半包含肿块，被定为异常图像，一半没有病变。然后计算以下纹理描述符，以分析感兴趣区域（ROI）的纹理图案：熵，能量，总和平均值，总和方差和聚类趋势。为了为每种方法选择最佳的功能集，我们应用了遗传算法（GA）。在ROI分类阶段，我们使用了随机森林算法，这是一种数据挖掘技术，可将数据分为非重叠段。实验结果表明，使用基于遗传算法的小波特征提取来选择最相关的特征可获得最佳分类率，AUC为0.90。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号