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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >Information Theory-Based Detection of Noisy Bit Planes in Medical Images
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Information Theory-Based Detection of Noisy Bit Planes in Medical Images

机译：基于信息论的医学图像噪声位平面检测

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摘要

Mammographic Computer-Aided Diagnosis systems are applications designed to assist radiologists in diagnosis of malignancy in mammographic findings. Most methods described in the literature do not perform a proper preprocessing step in mammographic images prior to classification, which can generate inconsistent results due to the potentially large amount of noise in medical images. This paper proposes a new method based on Information Theory and Data Compression for detection of random noise in image bit planes. In order to validate the efficiency of the proposed noise removal method, we used Machine Learning algorithms to classify mammographic findings from the Digital Database for Screening Mammography. Results using texture features indicate that a reduction in the radiometric resolution of 4 or 5 bit planes in digitized screen film mammographic images result in a better classification performance.
机译：乳腺X射线摄影计算机辅助诊断系统是旨在帮助放射科医生诊断乳腺X射线摄影发现的恶性肿瘤的应用程序。文献中描述的大多数方法在进行分类之前在乳房X线照片中没有执行适当的预处理步骤，由于医学图像中可能存在大量的噪声，因此可能会产生不一致的结果。提出了一种基于信息论和数据压缩的图像位平面随机噪声检测新方法。为了验证所提出的噪声消除方法的效率，我们使用了机器学习算法对来自乳腺X线筛查数字数据库的乳腺X线检查结果进行分类。使用纹理特征的结果表明，数字化的屏幕胶片乳房X线照片中4位或5位平面的辐射分辨率降低会导致更好的分类性能。

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