Content based neuro image classification

基于内容的神经图像分类

• 批准号: 6794776
• 负责人: USHA SINHA
• 金额: $ 9.34万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6794776
• 关键词: automated medical record system; bioimaging /biomedical imaging; brain imaging /visualization /scanning; brain neoplasms; computer program /software; data collection methodology /evaluation; data management; human data; information retrieval; interactive multimedia; magnetic resonance imaging; neoplasm /cancer classification /staging; training aid

DESCRIPTION (provided by applicant): The goal of this proposal is the automated classification of imaging studies of patients with tumors. As the role of imaging becomes increasingly important in medical care, effective methods for storing and retrieving key images will become critical. Image classification and subsequent summarization proffers a method to compress imaging studies by selecting only pertinent image slices that objectively document a patient's condition, while preserving the full integrity of the original data; as such, its applications include multimedia electronic medical records, telemedicine, and teaching files. This proposal details an innovative method to accomplish image classification based on principal component analysis. A training set of images classified by experts will be used to generate a basis set of images that captures the variance among the images. The projection on this basis set of images, called eigenimages, is used as an image index for classification and retrieval. Two key aspects critical to the success of accurate image classification are described: normalization of both image spatial and intensity properties. A modification to this methodology is also proposed to handle images with small abnormalities: image sub-regions that are 'abnormal' are located by searching the query image for the region that best matches a training set of sub-images of 'abnormal regions'. The target domain for the proposal is MR imaging studies of patients with brain tumors; in future work, this research will be extended to cover other neurological conditions, imaging modalities, and anatomical regions. Technical evaluation will be performed by comparing the automated methods with that of experts.

描述（由申请人提供）：该提案的目的是对肿瘤患者的成像研究的自动分类。随着成像的作用在医疗中变得越来越重要，存储和检索关键图像的有效方法将变得至关重要。图像分类和随后的摘要通过仅选择客观地记录患者状况的相关图像切片，同时保留原始数据的全部完整性，从而为压缩成像研究提供了一种方法；因此，其应用包括多媒体电子病历，远程医疗和教学文件。 该建议详细介绍了一种基于主成分分析的创新方法来完成图像分类。 由专家分类的培训集将用于生成一组基础图像集，以捕获图像之间的差异。此基础图像集的投影（称为特征图）用作分类和检索的图像索引。描述了对准确图像分类成功至关重要的两个关键方面：图像空间和强度属性的归一化。还提出了对这种方法的修改，以处理具有较小异常的图像：通过搜索最适合“异常区域”子图像训练集的区域的查询图像来找到“异常”的图像子区域。该建议的目标结构域是对脑肿瘤患者的MR成像研究。在未来的工作中，这项研究将扩展到涵盖其他神经系统状况，成像方式和解剖区域。技术评估将通过将自动化方法与专家的方法进行比较。