NORMAL IMAGE RECOGNITION TECHNICS FOR DIGITAL MAMMOGRAMS

描述(逐字摘自申请者摘要)：这个项目是一个 旨在开发自动化技术的程序的主要组成部分 用于在高分辨率数字中识别临床正常的乳房X光照片 乳房X光摄影(DM)。目标是可靠地识别50%的正常图像 同时不会将异常图像错误分类为正常图像。在临床上，这种方法 可以被认为是第二种意见策略或作为减少工作量 机制，因为大多数图像都是正常的。具体地说，这个提案 涉及两个相辅相成的研究领域：(1)对新的 一种用于正常图像识别的多分辨率统计检测方法 与微钙化有关的尺度区域，以及(2)可行性研究 旨在发展对正常组织区域的统计理解，在 与质量相关的比例。第一个区域涉及使用以下命令扩展图像 小波分析为图像的总和，每个图像包含不同级别的 细节或比例。每个扩展组件都可以用简单的 参数概率模型，而不是复杂原始数据的分析 图像。根据最大似然参数进行的统计检验可以是 派生的，将允许在比例上确定正常图像区域 与钙化有关的。这项测试是在两所大学独立进行的 相关标度，并将结果进行组合。如果所有图像区域都通过 正常性测试图像可以被宣布为临床正常相对于 钙化，与模型显著偏离的区域是 被认为可疑的。这项建议的第二个组成部分涉及 新的成像模型的研究。初步证据表明 乳房X光照片可以被认为是由简单的线性滤波产生的 进程。也就是说，滤波会产生不规则的图像特征。一个 反卷积方法产生了可以分析的规则随机场 用参数方法。将对这一领域进行研究，以期发展 大范围的正常组织检测方法，并应用于 以类似于第一个研究区域的方式包含质量。 这项研究的第二个好处包括开发参数描述 数字化乳房X光检查和参数分析方法，将转化为 许多DM应用，包括从直接X射线检测获取的图像 成像。

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.