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首页> 外文会议>Sensing and analysis technologies for biomedical and cognitive applications 2016 >Early Breast Cancer Detection with Digital Mammograms Using Haar-like Features and AdaBoost Algorithm
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Early Breast Cancer Detection with Digital Mammograms Using Haar-like Features and AdaBoost Algorithm

机译：使用类似Haar的特征和AdaBoost算法通过数字化X线乳房造影术进行早期乳腺癌检测

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摘要

The current computer-aided detection (CAD) methods are not sufficiently accurate in detecting masses, especially in dense breasts and/or small masses (typically at their early stages). A small mass may not be perceived when it is small and/or homogeneous with surrounding tissues. Possible reasons for the limited performance of existing CAD methods are lack of multiscale analysis and unification of variant masses. The speed of CAD analysis is important for field applications. We propose a new CAD model for mass detection, which extracts simple Haar-like features for fast detection, uses AdaBoost approach for feature selection and classifier training, applies cascading classifiers for reduction of false positives, and utilizes multiscale detection for variant sizes of masses. In addition to Haar features, local binary pattern (LBP) and histograms of oriented gradient (HOG) are extracted and applied to mass detection. The performance of a CAD system can be measured with true positive rate (TPR) and false positives per image (FPI). We are collecting our own digital mammograms for the proposed research. The proposed CAD model will be initially demonstrated with mass detection including architecture distortion.
机译：当前的计算机辅助检测（CAD）方法在检测肿块方面，尤其是在密实的乳房和/或较小的肿块（通常处于早期阶段）中，检测精度不够。当小块与周围组织小和/或均匀时，可能看不到小块。现有CAD方法性能有限的可能原因是缺乏多尺度分析和变体质量的统一。 CAD分析的速度对于现场应用很重要。我们提出了一种用于质量检测的新CAD模型，该模型提取简单的类似Haar的特征以进行快速检测，使用AdaBoost方法进行特征选择和分类器训练，应用级联分类器来减少误报，并利用多尺度检测来检测质量的不同大小。除Haar特征外，还提取局部二进制模式（LBP）和定向梯度直方图（HOG）并将其应用于质量检测。可以使用真阳性率（TPR）和每个图像假阳性（FPI）来测量CAD系统的性能。我们正在收集我们自己的数字乳房X线照片，以进行拟议的研究。拟议的CAD模型将首先通过包括架构变形在内的质量检测进行演示。

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