Integrated Image Analysis System for Epilepsy

癫痫综合图像分析系统

• 批准号: 6824523
• 负责人: HAMID SOLTANIAN-ZADEH
• 金额: $ 29.16万
• 依托单位: HENRY FORD HEALTH SYSTEM
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6824523
• 关键词: Internet; bioengineering /biomedical engineering; bioimaging /biomedical imaging; biomedical automation; brain electrical activity; brain imaging /visualization /scanning; brain morphology; clinical research; computer data analysis; computer human interaction; computer simulation; computer system design /evaluation; electroencephalography; epilepsy; hippocampus; human data; human subject; image guided surgery /therapy; image processing; informatics; magnetic resonance imaging; memory; neuropsychological tests; patient oriented research; three dimensional imaging /topography

DESCRIPTION (provided by applicant): With the ever-increasing role of medical images in diagnosis, treatment, and evaluation of treatment effects, extraction of quantitative information from these images and efficient use of the results have become a necessity. In recent years, others and we have developed novel two-dimensional (2D) and three-dimensional (3D) deformable models for a variety of image analysis applications in medicine and industry. We have also developed reliable automated methods for defining the initial shape of the model for segmentation and characterization of hippocampus from magnetic resonance imaging (MRI). These methods need to be extended and feature extraction methods developed to segment and characterize (i.e., determine multi-parametric intensity distribution, texture, shape, surface area, and volume of) brain structures such as hippocampus, amygdala, red nucleus, substantia nigra, globus pallidus, putamen, corpus callosum, and thalamus from MRI. In addition, new databases need to be developed to hold the results with other clinical information (e.g., textual data) in a manner that can be searched, retrieved, and queried conveniently from any computer station. The goal of this project is to develop novel approaches for the above needs. Developments will be done in the context of an important biomedical application and will localize, segment, and characterize hippocampus from MRI. The proposed database will be able to evaluate correlation between a variety of risk factors and post-operative outcomes. The methods will be tested; evaluated, and validated, using simulated images and clinical studies of epileptic patients. Clinical diagnosis based on EEG studies and surgery outcome will be used as "gold standards" for evaluation and validation of the image analysis methods. The proposed research will be a breakthrough in the development and application of computerized methods for medical image quantitation and object characterization, and will advance image analysis science in the direction of integrating knowledge-based systems, deformable models, texture analysis, and database technology. The proposed approach is applicable to the identification, segmentation, and characterization of other biological structures (e.g., lung, liver, kidneys, cells, neurons). It is also applicable to virtually any image analysis task for which object quantitation and characterization are used.

描述（由申请人提供）：医学图像在诊断，治疗和评估治疗效果中的不断增长，从这些图像中提取定量信息以及对结果的有效使用已成为必要。近年来，其他人和我们开发了新颖的二维（2D）和三维（3D）可变形模型，用于医学和行业中的各种图像分析应用。我们还开发了可靠的自动化方法，用于定义模型的初始形状，用于从磁共振成像（MRI）中分割和表征海马。这些方法需要扩展并开发出特征提取方法，以细分和表征（确定多参数强度分布，质地，形状，表面积和体积），例如海马，杏仁核，红色核，红色核，nigra，nigra nigra，nigra nigra，nigra nigra，blobus pallidus pallidus，pallidus pallidus，patamen palliDus，pursus palliDus，pursus callosum callosum和thalamus thalamus thalamus thalamus thalamus thalamus thalamus。此外，需要开发新的数据库，以通过可以从任何计算机站方便地搜索，检索和查询其他临床信息（例如文本数据）来保留结果。该项目的目的是为上述需求开发新颖的方法。发展将在重要的生物医学应用的背景下进行，并将定位，细分和表征MRI的海马。 所提出的数据库将能够评估各种风险因素和术后结果之间的相关性。这些方法将进行测试；使用模拟图像和癫痫患者的临床研究进行了评估和验证。基于脑电图研究和手术结果的临床诊断将用作评估和验证图像分析方法的“金标准”。拟议的研究将是用于医学图像定量和对象表征的计算机化方法开发和应用的突破，并将朝着整合基于知识的系统，可变形模型，纹理分析和数据库技术的方向发展图像分析科学。所提出的方法适用于其他生物结构（例如肺，肝，肾脏，细胞，神经元）的识别，分割和表征。它也适用于使用对象定量和表征的任何图像分析任务。