ENTROPY-BASED TISSUE DISCRIMINATORS

描述（由申请人提供）：本提案中解决的主要问题是开发和评价一种自动化非侵入性方法，以使用机器学习算法区分不同的正常和病理组织类型;机器学习的先前应用基于基本上基于能量的反向散射超声的特征。我们的方法将是基于提取特征的图像，其像素是由包含在段的背散射超声的熵。熵成像的独特属性表明，我们提出的自动化分析将是特别强大的在临床环境中的深层组织的歧视。

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.