ENTROPY-BASED TISSUE DISCRIMINATORS

描述(由申请人提供)：本提案中解决的主要问题是利用机器学习算法开发和评估自动非侵入性方法来区分不同的正常和病理组织类型；以前的机器学习应用一直基于本质上基于能量的背向散射超声的特征。我们的方法将基于从图像中提取特征，其像素由背向散射超声片段中包含的熵确定。熵成像的独特属性表明，我们提出的自动化分析将特别适用于临床环境中深层组织的区分。

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.