Digital Mammography: Advanced Computer-Aided Breast Can*

数字乳房X光检查：先进的计算机辅助乳房检查*

• 批准号: 6753540
• 负责人: HEANG-PING CHAN
• 金额: $ 49.26万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6753540
• 关键词: artificial intelligence; bioimaging /biomedical imaging; breast neoplasms; calcification; clinical research; computer assisted diagnosis; computer system design /evaluation; data management; diagnosis design /evaluation; diagnosis quality /standard; digital imaging; human data; information systems; mammography; mathematics; neoplasm /cancer diagnosis; neoplastic growth

DESCRIPTION (provided by applicant): The major goals of the proposed research are (1) to develop a computer-aided diagnosis (CAD) system for full field digital mammography (FFDM) using advanced computer vision techniques and (2) to evaluate the effects of CAD on interpretation of DMs. Previous CAD methods for lesion (mass and microcalcification) detection and characterization have been designed for digitized film mammograms and have generally been based on image features extracted from a single view. Our proposed approach is distinctly different from the previous approaches in that image information from two-view mammograms and bilateral mammograms will be fused using machine intelligence techniques. This fundamental change will expand the amount of information utilized in CAD and is expected to improve lesion detection and characterization. New computer vision techniques will be specifically designed for FFDM in order to exploit the advantages offered by digital detectors. This will produce a CAD system that is integrated with and takes full advantage of the latest imaging technologies to further improve the health care of women. We hypothesize that these advanced multiple-image information fusion techniques will lead to a more effective CAD system for FFDMs in comparison to a single-image approach, and that the CAD system will significantly improve radiologists' accuracy in the four most important areas of mammography: (i) detection of masses, (ii) classification of masses, (iii) detection of microcalcifications, and (iv) classification of microcalcifications. A database of digital mammograms (DMs) with malignant and benign lesions and a set of normal cases will be collected. We will first adapt our current film-based CAD algorithms to DMs in each of the four areas, taking into account the differences in the imaging characteristics between DMs and digitized mammograms. New computer vision techniques will then be developed to improve upon the current methods and to exploit the potential advantages of the high contrast sensitivity, high detective quantum efficiency, wide dynamic range, and the linear response to x-ray intensity of digital detectors. Novel regional registration methods for identifying corresponding lesions on CC and MLO views and for comparing the density symmetry on bilateral mammograms will be developed. Innovative fuzzy classification schemes will be designed to fuse multiple-image information and one-view information to reduce false positives and to improve detection sensitivity. Multiple-view morphological and texture features of a lesion will be merged using neural networks or other statistical classifiers for characterization of malignant and benign lesions. To test the hypotheses, we will (1) compare the performance of the multiple-image fusion CAD algorithm for DMs in each area to that of the corresponding one-view algorithm, (2) compare the detection accuracy of masses and microcalcifications on DMs with and without CAD by observer ROC studies, and (3) compare the classification accuracy of masses and microcalcifications on DMs with and without CAD by observer ROC studies. It is expected that this research will not only lead to an effective CAD system for FFDM, the multiple-image fusion approach and the new computer vision techniques will also advance CAD technology for mammography in general.

描述（由申请人提供）：拟议研究的主要目标是（1）使用先进的计算机视觉技术和（2）评估CAD对DMS解释的影响。先前用于病变（质量和微钙化）检测和表征的CAD方法已设计用于数字化膜X线照片，通常是基于从单视图中提取的图像特征。我们提出的方法与以前的方法明显不同，因为该图像信息来自两视乳房X线照片，并且使用机器智能技术融合了双边乳房X线照片。这种基本变化将扩大CAD中使用的信息量，并有望改善病变检测和表征。新的计算机视觉技术将专门为FFDM设计，以利用数字探测器提供的优势。这将产生与最新成像技术相结合并充分利用的CAD系统，以进一步改善女性的医疗保健。 We hypothesize that these advanced multiple-image information fusion techniques will lead to a more effective CAD system for FFDMs in comparison to a single-image approach, and that the CAD system will significantly improve radiologists' accuracy in the four most important areas of mammography: (i) detection of masses, (ii) classification of masses, (iii) detection of microcalcifications, and (iv) classification of microcalcifications.将收集具有恶性和良性病变以及一组正常情况的数字乳房X线照片（DMS）数据库。考虑到DMS和数字化乳房X线照片之间的成像特征的差异，我们将首先将目前的基于膜的CAD算法调整为四个区域的DMS。然后将开发新的计算机视觉技术，以改善当前方法，并利用高对比度灵敏度，高侦探量子效率，广泛的动态范围以及对数字检测器X射线强度的线性响应的潜在优势。将开发出用于识别CC和MLO视图上相应病变的新型区域注册方法以及比较双边乳房X线照片上的密度对称性。创新的模糊分类方案将旨在融合多图像信息和单视信息，以减少误报并提高检测灵敏度。病变的多形形态和纹理特征将使用神经网络或其他统计分类器合并，以表征恶性病变和良性病变。要测试假设，我们将（1）比较每个区域的多图像融合CAD算法的性能与相应的一视算法的表现，（2）比较质量和无需CAD的DMS的质量和微钙化的检测准确性，并与Observer Roc Restivic and（3）相比，以及3）的分类，以及（3）的分类，以及3）的分类精确度，以及3）的精确率，以及3）观察者ROC研究的CAD。可以预期，这项研究不仅会导致FFDM的有效CAD系统，多图像融合方法和新的计算机视觉技术也将推动乳房X线摄影的CAD技术。