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首页> 外文会议>International Workshop on Digital Mammography(IWDM 2006); 20060618-21; Manchester(GB) >Using Wavelet-Based Features to Identify Masses in Dense Breast Parenchyma
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Using Wavelet-Based Features to Identify Masses in Dense Breast Parenchyma

机译：使用基于小波的特征识别密集乳腺实质中的肿块

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摘要

Automated detection of masses on mammograms is challenged by the presence of dense breast parenchyma. The aim of this study is to investigate the feasibility of wavelet-based feature analysis in identifying spiculated and circumscribed masses in dense breast parenchyma. The method includes an edge detection step for breast border identification and employs Gaussian mixture modeling for dense parenchyma labeling. Subsequently, wavelet decomposition is performed and intensity as well as orientation features are extracted from approximation and detail subimages, respectively. Logistic regression analysis (LRA) is employed to differentiate spiculated and circumscribed masses from normal dense parenchyma. The proposed method is tested in 90 dense mammograms containing spiculated masses (30), circumscribed masses (30) and normal parenchyma (30). Free-response receiver operating characteristic (FROC) analysis is used to evaluate the performance of the method, achieving 83.3% sensitivity at 1.5 and 1.8 false positives per image for identifying spiculated and circumscribed masses, respectively.
机译：乳腺薄壁组织的存在挑战了乳房X线照片上的自动检测质量。这项研究的目的是调查基于小波的特征分析在确定乳腺实质内的弥漫性和外切性肿块中的可行性。该方法包括用于乳房边界识别的边缘检测步骤，并采用高斯混合建模进行密集的实质标记。随后，执行小波分解，并分别从近似子图像和细节子图像中提取强度和方向特征。使用逻辑回归分析（LRA）来区分正常和密集的薄壁组织中的弥漫性和外切性肿块。所提出的方法在90个密集乳腺X线照片中进行了测试，其中包含弥漫性肿块（30），外接肿块（30）和正常薄壁组织（30）。使用自由响应接收器操作特征（FROC）分析来评估该方法的性能，在每幅图像1.5和1.8假阳性时，分别可识别尖峰和外切肿块，灵敏度达到83.3％。

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