COMPUTERIZED MAMMOGRAPHIC LESION DESCRIPTION

描述（来自申请人摘要的逐字记录）：乳房X线摄影被认为是 降低乳腺癌死亡率的重要手段。然而，其准确性 有限的，无论是在敏感性（一些癌症错过）和特异性（许多 非癌症病例被转诊进行侵入性手术）。这项建议旨在 提高乳腺癌筛查的有效性， 可以从数字乳房X线照片中获得的测量结果，以及 分类器，导致计算机自动生成的描述， 可疑区域。特别地，用于标准化词典的分类器 肿块形状、肿块边缘和肿块密度以及乳房成分将被 设计了 这些自动生成的描述旨在增加 通过为放射科医生提供 对病变进行局部治疗。这些描述也应该有助于 与计算机程序相结合，通过拒绝 那些检测到的不太可能代表癌症的区域（假阳性 减少）。此外，对乳房摄影病灶的自动描述将 减少阅片者的变异性，并可能有助于培训放射科医生使用 标准化词典 该项目将首先开发一个分割程序， 乳房X光检查的肿块该方法将是一个多阶段的知识指导 系统从数字乳房X光片中进行的测量利用了这一点。 边界，并包括一些已在文献中报道，以及 新提出的措施。作为乳房X光检查的分类问题 质量是非常困难的，混合分类器将被构造， 多个分类器的特定能力的优势。基于规则的系统 为每个BIRADS做出综合决策 描述。最后，这些描述将在分类器中使用， 确定恶性肿瘤的可能性。 该项目的成果将是开发对非专家的援助的工具。 乳房摄影师定义乳房摄影肿块的特征，并帮助 决定活检乳房X线摄影病变，但也可以应用于帮助 通过计算机辅助诊断提高敏感性， 工具.

DESCRIPTION (Verbatim from Applicant's Abstract): Mammography is recognized as an important means to reduce breast cancer mortality. However, its accuracy is limited, both in sensitivity (some cancers are missed) and specificity (many non-cancer cases are referred for invasive procedures). This proposal aims at improving the effectiveness of breast cancer screening by the discovery of measurements that can be taken from digital mammograms, and the design of classifiers that result in an automatic computer-generated description of suspicious areas. In particular, classifiers for the standardized lexicon for mass shape, mass margin and mass density, as well as breast composition will be designed. These automaticaily generated descriptions are aimed at increasing the specificity of mammography by providing the radiologist with a probability of rnalignancy for the lesion. The descriptions should also be helpful in conjunction with computer programs that detect suspicious areas, by rejecting those detected areas that do not likely represent cancer (false positive reduction). In addition, automatic description of a marnmographic iesion will reduce reader variability and may heip in training radiologists to use the standardized lexicon. This project will first develop a segmentation program, that finds the border of a mammographic mass. The approach will be a multi-stage knowledge guided system. Measurements to be taken from digital mammograms make use of this border, and include some that have been reported in the literature as well as newly proposed measurements. As the classification problem for mammographic masses is very difficult, hybrid classifiers will be constructed that take advantage of specific abilities of multiple classifiers. A rule based system will be developed to arrive at an aggregate decision for each of the BIRADS descriptions. Finally, these descriptions will be used in a classifier to determine the likelihood of malignancy. The outcome of this project would be the tools to develop an aid to non-expert mammographers to define the characteristics of mammographic masses and help in decisions to biopsy a mammographic lesion, but can also be applied to help enhance sensitivity through computer assisted diagnosis, and in educational tools.