ENTROPY-BASED TISSUE DISCRIMINATORS

基于熵的组织鉴别器

基本信息

批准号：
8737902
负责人：
SAMUEL A WICKLINE
金额：
$ 18.43万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
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/8737902
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 data reduction design detector heart motion indexing meetings n-dimensional novel public health relevance radiofrequency signal processing sound tissue processing vector

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 data reduction design detector heart motion indexing meetings n-dimensional novel public health relevance radiofrequency signal processing sound tissue processing vector

展开

项目摘要

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对使用机器学习算法区分不同正常和病理组织类型的自动化无创方法的开发和评估；机器学习的先前应用是基于基本基于能量的反向散射超声的功能。我们的方法将基于从图像中提取特征的特征，其像素是由反向散射超声段中包含的熵确定的。熵成像的独特属性表明，我们提出的自动分析对于在临床环境中歧视深组织尤其强大。