COMPUTER-AIDED DIAGNOSIS IN DIGITAL MAMMOGRAPHY

描述(改编自申请人摘要)：建议的目标 研究是开发计算机辅助诊断(CAD)方案以帮助 放射科医生通过乳房X光检查诊断乳腺癌。的假说 这个项目是CAD将提高放射科医生的解释能力 乳房X光检查，因此漏诊癌症的数量和 不必要地送去做活组织检查的妇女可以减少。这项建议是 专门用于开发用于检测和 指端微钙化的分类(良性与恶性) 乳房X光检查。该项目将继续完善现有的CAD方案， 朝着临床可行的CAD方案的目标迈进。的具体目标 这一建议是：(1)提高检测方案的性能 通过根据最亮的微钙化来检测细微的星团； 通过消除由以下原因引起的误报来减少误报 钙化血管或明显的良性钙化；并通过改善 方案的稳健性；(2)研究评分方法对 测量一个检测方案的性能，然后设计出“最佳”方案 针对簇状微钙化的评分方法；(3)改善 分类方案的性能：通过扩大我们的数据库 良性和恶性病例；通过识别额外的计算机提取的 特征；以及通过利用来自多个来源的信息：放大 视图、同一乳房的两个视图和同一乳房的相同视图 不同时间拍摄；(4)检测与分类相结合 方案，从而完全自动化分类方案。这将是 需要检查对不完整分类方案的影响 检测一簇内的所有微钙化，为假阳性 信号被包括在真簇和假阳性簇中。 利用这些信息，申请者建议开发一个界面 在检测和分类方案之间；以及(5)执行 该分类方案的初步临床评价。

DESCRIPTION (Adapted from Applicant's Abstract): The goal of the proposed research is to develop computer-aided diagnosis (CAD) schemes to assist radiologists in diagnosing breast cancer from mammograms. The hypothesis of this project is that CAD will improve radiologists' ability to interpret mammograms, so that both the number of missed cancers and the number of women unnecessarily sent to biopsy can be reduced. This proposal is specifically for the development of CAD schemes for the detection and the classification (benign versus malignant) of microcalcifications from digital mammograms. This project will continue to refine the current CAD schemes, towards our goal of clinically viable CAD schemes. The specific aims of this proposal are: (1) To improve the performance of our detection scheme by detecting subtle clusters based on their brightest microcalcifications; by decreasing the number of false positives by eliminating those caused by calcified vessels or obvious benign calcifications; and by improving the robustness of the scheme; (2) To study the effect of scoring methodology on the measured performance of a detection scheme and then to devise the "best" scoring method for clustered microcalcifications; (3) To improve the performance of the classification scheme: by enlarging our database of benign and malignant cases; by identifying additional computer-extracted features; and by utilizing information from multiple sources: magnification views, two views of the same breast, and the same views of the same breast taken at different times; (4) To combine the detection and classification schemes, thereby fully automating the classification scheme. This will entail examining the effects on the classification scheme of incomplete detection of all the microcalcifications within a cluster, of false positive signals being included in a true cluster, and of false positive clusters. Using this information, the applicants proposed to develop an interface between the detection and classification schemes; and (5) To perform a preliminary clinical evaluation of the classification scheme.