NORMAL IMAGE RECOGNITION TECHNICS FOR DIGITAL MAMMOGRAMS

数字乳房X线照片的正常图像识别技术

• 批准号: 6173746
• 负责人: JOHN J HEINE
• 金额: $ 14.35万
• 依托单位: UNIVERSITY OF SOUTH FLORIDA
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-07-02 至 2002-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6173746
• 关键词: bioimaging /biomedical imaging; biomedical equipment development; computer assisted diagnosis; diagnosis design /evaluation; digital imaging; female; human data; image enhancement; mammography; mathematical model; model design /development

DESCRIPTION (Verbatim from the Applicant's Abstract): This project is a principal component of a program directed at developing automated techniques for the recognition of clinically normal mammograms in high resolution digital mammography (DM). The goal is to reliably recognize 50% of the normal images while not misclassifying abnormal images as normal. Clinically, the approach can be considered as a second opinion strategy or as a work load reduction mechanism since the majority of images are normal. Specifically, this proposal involves two areas of complementary research: (1) A thorough evaluation of new multresolution statistical detection method used for identifying normal image regions at scales relevant to microcalcifications, and (2) A feasibility study directed at developing a statistical understanding of normal tissue regions at scales relevant to masses. The first area involves expanding the image with wavelet analysis into a sum of images each containing different levels of detail or scale. Each expansion component can be analyzed with simple parametric probability models as opposed to the analysis of the complicated raw image. A statistical test following from maximum likelihood arguments can be derived that will allow the determination of normal image regions at scales relevant to calcifications. The test is applied independently at the two relevant scales, and the results are combined. If all image regions pass the normality test the image can be declared clinically normal with respect to calcifications, and areas that deviate significantly from the model are considered as suspicious. The second component of this proposal involves the investigation of new image formation model. Initial evidence indicates that mammograms can be considered as resulting from a simple linear filtering process. That is the filtering induces the irregular image characteristics. A deconvolution approach results in a regular random field that can be analyzed with parametric methods. This field will be studied with aim of developing normal tissue detection methods at large scales and applied to images that contain masses in an analogous fashion to that of the first research area. Secondary benefits of this study include developing a parametric description of digitized mammograms and parametric analysis methods that will translate to many DM applications including images acquired from direct X-ray detection imaging.

描述（逐字研究申请人的摘要）：此项目是一个 旨在开发自动化技术的计划的主要组成部分 为了识别高分辨率数字的临床正常乳房X线照片 乳房X线摄影（DM）。目的是可靠地识别50％的正常图像 虽然不会将异常图像误认为是正常的。在临床上，方法 可以被视为第二意见策略或减少工作负荷 机理由于大多数图像是正常的。具体来说，该提议 涉及互补研究的两个领域：（1）对新的全面评估 用于识别正常图像的Multresolution统计检测方法 与微钙化相关的尺度区域，以及（2）可行性研究 致力于对正常组织区域的统计理解 与群众相关的尺度。第一个领域涉及扩展图像 小波分析分为每一个包含不同级别的图像的总和 细节或比例。每个扩展组件都可以通过简单分析 参数概率模型与复杂原始的分析相反 图像。最大似然参数的统计测试可以是 得出的将允许在尺度上确定正常图像区域 与钙化有关。测试是在两个下独立应用的 相关量表，结果合并。如果所有图像区域都通过 正态性测试该图像可以在临床上声明 钙化以及明显偏离模型的区域是 被认为是可疑的。该提案的第二部分涉及 研究新图像形成模型。最初的证据表明 可以将乳房X线照片视为由简单的线性过滤产生的 过程。那就是滤波会引起不规则的图像特征。一个 反卷积方法导致常规随机场，可以分析 使用参数方法。该领域将以开发的目的进行研究 大规模的正常组织检测方法，并应用于图像 与第一个研究领域的质量相似。 这项研究的次要好处包括开发一个参数描述 数字化的乳房X线照片和参数分析方法将转化为 许多DM应用程序，包括从直接X射线检测中获取的图像 成像。