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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：基于信息理论的医学图像中嘈杂钻头平面的检测

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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：乳房XMPoxt计算机辅助诊断系统是旨在协助放射科医师在乳房X线切调查结果中诊断恶性肿瘤的应用。在文献中描述的大多数方法在分类之前不在乳房X线图像中执行适当的预处理步骤，这可能导致由于医学图像中的潜在噪声量的潜在噪声而产生不一致的结果。本文提出了一种基于信息理论和数据压缩的新方法，用于检测图像位平面中的随机噪声。为了验证所提出的噪声清除方法的效率，我们使用了机器学习算法来分类来自数字数据库的乳房X线切检查释放乳房X线摄影。使用纹理特征的结果表明数字化屏幕乳房X线图像中4或5位平面的辐射分辨率的降低导致更好的分类性能。

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