DEVELOPMENT OF COMPUTER BASED TECHNIQUES IN MAMMOGRAPHY

乳房X线照相术中基于计算机的技术的发展

• 批准号: 2376830
• 负责人: HEANG-PING CHAN
• 金额: $ 38.84万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-05-03 至 2001-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2376830
• 关键词: artificial intelligence; breast neoplasm /cancer diagnosis; breast neoplasms; computational neuroscience; computer assisted diagnosis; computer assisted medical decision making; computer program /software; diagnosis design /evaluation; diagnosis quality /standard; digital imaging; image processing; mammography; mathematical model

The goal of the proposed research is to develop computer-aided diagnosis (CAD) schemes in order to improve the diagnostic accuracy of breast cancer in mammography. Four specific aims are included: (l) development of computer programs for the detection and characterization of microcalcifications, (2) development of computer programs for detection and characterization of masses, (3) implementation of the CAD algorithms in a dedicated workstation to perform a pilot preclinical testing of the accuracy of the CAD programs, and (4) evaluation of the effects of the CAD schemes on radiologists' performance. The proposed CAD schemes will aid radiologists in screening mammograms for suspicious lesions and provide estimate of the likelihood of malignancy for the detected lesions. The information is expected to reduce the miss rate and to improve the positive predictive value of the mammographic findings. A data base of clinical mammograms which include malignant and benign microcalcifications and masses will be established. Physical measures which characterize the significant image features of the lesions will be developed. Based on these measures, linear discriminant classifiers or neural network classifiers will be optimized using a genetic algorithm approach to classify true and false signals and to estimate the likelihood of malignancy for each type of lesions. For automated detection and classification of microcalcifications, we will investigate the usefulness of multiresolution analysis for enhancement of the signal-to-noise ratio of the microcalcifications and for improvement of feature extraction techniques. Physical characteristics such as size, shape, frequency spectrum, spatial distribution, clustering properties, and texture features will be extracted and analyzed with the classifiers. For automated detection and classification of masses, we will improve the background correction and signal segmentation techniques, and develop effective false-positive reduction methods. Adaptive filtering, edge enhancement, and clustering segmentation methods will be developed for extraction of the mass margins. Physical characteristics such as size, density, edge sharpness, calcifications, shape, lobulation, spiculation, and multiresolution wavelet texture features will be extracted from the masses and analyzed with the classifiers. The algorithms will be implemented in a dedicated CAD workstation and preclinical testing will be conducted. The performance of the programs in a clinical setting will be assessed. The algorithms will be revised and improved based on the information obtained with the preclinical testing. The study is a vital step for the development of a clinically reliable CAD scheme. Observer performance studies using receiver operating characteristic (ROC) methodology will be conducted to evaluate the effects of the CAD schemes on radiologists'performance.

拟议研究的目的是开发计算机辅助诊断 （CAD）方案以提高乳腺癌的诊断准确性 在乳房X线摄影。包括四个具体目标：（l）开发 用于检测和表征的计算机程序 微钙化，（2）开发计算机程序检测 和质量的表征，（3）CAD算法的实现 在专门的工作站中，以进行飞行员的临床前测试 CAD程序的准确性以及（4）评估CAD的影响 放射学家表现方案。拟议的CAD方案将有助于 放射科医生在筛查可疑病变的乳房X线照片并提供 估计检测病变的恶性肿瘤的可能性。这 预计信息将降低错过率并改善 乳腺X线摄影结果的正预测价值。 临床乳房X线照片的数据库，包括恶性和良性 将建立微钙化和质量。身体措施 哪些特征是病变的重要图像特征 发达。基于这些措施，线性判别分类器或 神经网络分类器将使用遗传算法进行优化 对真实和错误信号进行分类并估算可能性的方法 每种类型病变的恶性肿瘤。 为了自动检测和微钙化分类，我们将 研究多分析分析的有用性 微钙化的信噪比和改进 特征提取技术。物理特征，例如大小 形状，频谱，空间分布，聚类特性， 和分类器将提取和分析纹理功能。 对于自动检测和质量分类，我们将改善 背景校正和信号分割技术，并发展 有效的假阳性还原方法。自适应过滤，边缘 将开发增强和聚类分割方法 提取质量边缘。物理特征，例如大小 密度，边缘清晰度，钙化，形状，小叶，杂音， 并从 群众并与分类器进行分析。 该算法将在专用的CAD工作站中实施，并且 将进行临床前测试。程序在 将评估临床环境。该算法将进行修订，并 根据临床前测试获得的信息得到改进。 该研究是开发临床可靠CAD的重要步骤 方案。 使用接收器操作特征（ROC）的观察者绩效研究 将进行方法来评估CAD方案的影响 关于放射科医生的表现。