ENTROPY-BASED TISSUE DISCRIMINATORS

描述（由申请人提供）：本提案中解决的主要问题是使用机器学习算法开发和评估一种自动无创方法来区分不同的正常和病理组织类型；机器学习以前的应用是基于后向散射超声的特征，这些特征本质上是基于能量的。我们的方法将基于从图像中提取特征，这些图像的像素由后向散射超声片段中包含的熵决定。熵成像的独特属性表明，我们提出的自动分析将在临床环境中对深部组织的区分特别稳健。

DESCRIPTION (provided by applicant): The major problem addressed in this proposal is the development and evaluation of an automated noninvasive approach to discriminate different normal and pathological tissue types using machine learning algorithms; previous applications of machine learning have been based on features of the backscattered ultrasound that are essentially energy based. Our approach will be based on extracting features from images whose pixels are determined by the entropy contained in segments of the backscattered ultrasound. The unique attributes of entropy imaging suggest that the automated analysis we propose would be particularly robust for discrimination of deep tissues in a clinical environment.