Digital Mammography: Advanced Computer-Aided Breast Can*

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全视野数字乳房x线摄影（FFDM）的计算机辅助诊断（CAD）系统，(2)评估CAD对dm解释的影响。以前用于病变（肿块和微钙化）检测和表征的CAD方法是为数字化乳房x光片设计的，并且通常基于从单一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光片和双侧乳房x光片的图像信息将使用机器智能技术融合。这一根本性的变化将扩大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，以利用数字检测器提供的优势。这将产生一个综合并充分利用最新成像技术的CAD系统，以进一步改善妇女的保健。我们假设这些先进的多图像信息融合技术将为ffdm带来比单图像方法更有效的CAD系统，并且CAD系统将显著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)肿块检测，（ii）肿块分类，（iii）微钙化检测，（iv）微钙化分类。将收集包含恶性和良性病变以及一组正常病例的数字乳房x线照片（DMs）数据库。我们将首先将目前基于胶片的CAD算法应用于这四个领域的dm，同时考虑到dm和数字化乳房x光片之间成像特征的差异。新的计算机视觉技术将在现有方法的基础上得到发展，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等潜在优势。将开发新的区域配准方法，用于在CC和MLO视图上识别相应的病变，并比较双侧乳房x光片上的密度对称性。将设计创新的模糊分类方案，融合多图像信息和单视图信息，以减少误报，提高检测灵敏度。病变的多视图形态和纹理特征将使用神经网络或其他统计分类器合并，以表征恶性和良性病变。为了验证这些假设，我们将(1)比较每个区域dm的多图像融合CAD算法与相应的单视图算法的性能，(2)通过观察者ROC研究比较有CAD和没有CAD的dm上肿块和微钙化的检测精度，以及(3)通过观察者ROC研究比较有CAD和没有CAD的dm上肿块和微钙化的分类精度。预计本研究不仅将导致一个有效的FFDM 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CAD技术的发展。

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.