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开发用于全视野数字乳房X线摄影（FFDM）的计算机辅助诊断（CAD）系统，以及（2）评估效果CAD 对 DM 的解释。以前用于病变（肿块和微钙化）检测和表征的 CAD 方法是针对数字化胶片乳房 X 光照片而设计的，并且通常基于从单个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，因为来自双视图乳房 X 光检查和双边乳房 X 光检查的图像信息将使用机器智能技术融合。这一根本性变化将扩大 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。这将产生一个集成并充分利用最新成像技术的 CAD 系统，以进一步改善妇女的医疗保健。我们假设，与单图像方法相比，这些先进的多图像信息融合技术将为 FFDM 带来更有效的 CAD 系统，并且 CAD 系统将显着提高放射科医生在乳房 X 线摄影四个最重要领域的准确性： (i) 肿块的检测，(ii) 肿块的分类，(iii) 微钙化的检测，以及(iv) 微钙化的分类。将收集具有恶性和良性病变的数字乳房X线照片（DM）和一组正常病例的数据库。我们首先将当前基于胶片的 CAD 算法应用于 DM 的四个领域，同时考虑到 DM 和数字化乳房 X 光检查之间成像特性的差异。然后将开发新的计算机视觉技术，以改进当前的方法，并利用数字探测器的高对比灵敏度、高探测量子效率、宽动态范围和对 X 射线强度的线性响应的潜在优势。将开发新的区域配准方法，用于识别 CC 和 MLO 视图上的相应病变以及比较双侧乳房 X 光照片上的密度对称性。将设计创新的模糊分类方案来融合多图像信息和单视图信息，以减少误报并提高检测灵敏度。将使用神经网络或其他统计分类器合并病变的多视图形态和纹理特征，以表征恶性和良性病变。为了检验假设，我们将 (1) 将每个区域的 DM 多图像融合 CAD 算法的性能与相应的单视图算法的性能进行比较，(2) 将 DM 上肿块和微钙化的检测精度与以及（3）通过观察者 ROC 研究比较有和没有 CAD 的 DM 上肿块和微钙化的分类准确性。预计这项研究不仅会为 FFDM 带来有效的 CAD 系统，多图像融合方法和新的计算机视觉技术也将总体上推进乳腺 X 线摄影的 CAD 技术。