ENTROPY-BASED TISSUE DISCRIMINATORS

基于熵的组织鉴别器

• 批准号: 8636638
• 负责人: MICHAEL Scott HUGHES
• 金额: $ 22.8万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8636638
• 关键词: Address; Algorithms; Back; Base Composition; Bayesian Analysis; Cardiac; Classification; Classification Scheme; Clinical; Color; Data; Data Set; Databases; Detection; Development; Diffuse; Dimensions; Discrimination; Disease; Ensure; Entropy; Environment; Evaluation; Expeditions; Fatty Liver; Fibrosis; Fishes; Foundations; Fractals; Frequencies; Heart; Histocompatibility Testing; Image; Image Analysis; Individual; Infarction; Injury; Investigation; Ischemia; Joints; Kidney; Knowledge; Label; Liver; Machine Learning; Maps; Measures; Methods; Metric; Microscopic; Normal tissue morphology; Organ; Outcome; Pathology; Pattern; Pharmaceutical Preparations; Physiological; Positioning Attribute; Procedures; Process; Property; Prostate; Radio; Reporting; Resolution; Rodent; Scheme; Shapes; Signal Transduction; Specificity; Staining method; Stains; Stream; Structure; Testing; Time; Tissue Differentiation; Tissue Model; Tissues; Ultrasonic Transducer; Ultrasonics; Ultrasonography; Visual Cortex; Work; attenuation; base; computerized data processing; data reduction; design; detector; heart motion; indexing; meetings; n-dimensional; novel; public health relevance; radiofrequency; sound; tissue processing; vector

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.

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。