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    • 1. 发明授权
    • Multiple image fusion
    • 多重图像融合
    • US07333645B1
    • 2008-02-19
    • US10973837
    • 2004-10-26
    • Richard A. MitchellMichael J. Collins
    • Richard A. MitchellMichael J. Collins
    • G06K9/00
    • G06K9/3233G06K2209/053G06T7/0012G06T2207/10116G06T2207/30068Y10S128/92Y10S128/922
    • The method and system for exploiting information from multiple images in a mammographic computer-aided detection application is disclosed. A pair of images is obtained by a CAD system. The images are processed to produce a set of regions of interest (ROIs) to be associated with each image. A ROI is selected from the first image of the pair. This ROI is identified and matched to a ROI in the second image. The single image feature values are obtained by the two ROIs of the image pair. Transforming the image feature value to an integer value produces a pair of integers for each image feature value. The pair of integers defines the element of the pre-determined co-occurrence matrix. An element of a predetermined co-occurrence matrix is selected to provide evidence value for the ROI of the first image.
    • 公开了一种在乳房X光计算机辅助检测应用中利用来自多个图像的信息的方法和系统。 通过CAD系统获得一对图像。 处理图像以产生要与每个图像相关联的一组感兴趣区域(ROI)。 从该对的第一个图像中选择ROI。 该ROI被识别并与第二图像中的ROI匹配。 单个图像特征值由图像对的两个ROI获得。 将图像特征值转换为整数值可为每个图像特征值生成一对整数。 这对整数定义了预先确定的同现矩阵的元素。 选择预定同现矩阵的元素以提供第一图像的ROI的证据值。
    • 2. 发明授权
    • Method for analyzing detections in a set of digital images using case based normalcy classification
    • 使用基于案例的正常分类来分析一组数字图像中的检测的方法
    • US06763128B1
    • 2004-07-13
    • US10461316
    • 2003-06-13
    • Steven K. RogersMichael J. CollinsRichard A. Mitchell
    • Steven K. RogersMichael J. CollinsRichard A. Mitchell
    • B06K900
    • G06K9/6293G06K9/4609G06K9/56G06K2209/05G06T7/0012G06T2207/30068
    • A computer aided detection method and system to assist radiologists in the reading of medical images. The method and system has particular application to the area of mammography including detection of clustered microcalcifications and densities. A microcalcification detector is provided wherein individual detections are rank ordered and classified, and one of the features for classification is derived using a multilayer perceptron. A density detector is provided including an iterative, dynamic region growing module with embedded subsystem for rank ordering and classification of a best subset of candidate masks. A post processing stage is provided where detections are analyzed in the context of a set of images for a patient. The post processing includes a normalcy classification including providing computed values corresponding to each detection from a category of detections on an image set, computing a normalcy value using the computed values, and removing all detections from an image set when the normalcy value does not meet a predetermined condition. The final output of the system is a set of indications overlaid on the input medical images.
    • 一种计算机辅助检测方法和系统,用于帮助放射科医师阅读医学图像。 该方法和系统在乳腺摄影领域具有特殊应用,包括检测聚类微钙化和密度。 提供了一种微钙化检测器,其中单独的检测是排序和分类的,并且使用多层感知器导出用于分类的特征之一。 提供了一种密度检测器,其包括具有嵌入式子系统的迭代动态区域增长模块,用于对候选掩模的最佳子集进行排序和分类。 提供后处理阶段,其中在用于患者的一组图像的上下文中分析检测。 后处理包括正常分类,包括从图像集中的检测类别提供与每个检测相对应的计算值,使用计算值计算正常值,以及当正常值不满足时从图像集中移除所有检测 预定条件。 系统的最终输出是覆盖在输入医学图像上的一组指示。
    • 3. 发明授权
    • Computer aided detection of masses and clustered microcalcifications with single and multiple input image context classification strategies
    • 计算机辅助检测群体和群集微钙化与单输入和多输入图像上下文分类策略
    • US06801645B1
    • 2004-10-05
    • US09602762
    • 2000-06-23
    • Michael J. CollinsSteven K. RogersRichard A. Mitchell
    • Michael J. CollinsSteven K. RogersRichard A. Mitchell
    • G06K900
    • G06K9/6293G06K9/4609G06K9/56G06K2209/05G06T7/0012G06T2207/30068
    • A computer aided detection method and system to assist radiologists in the reading of medical images. The method and system has particular application to the area of mammography including detection of clustered microcalcifications and densities. A microcalcification detector is provided wherein individual detections are rank ordered and classified, and one of the features for classification is derived using a multilayer perceptron. A density detector is provided including an iterative, dynamic region growing module with embedded subsystem for rank ordering and classification of a best subset of candidate masks. A post processing stage is provided where detections are analyzed in the context of a set of images for a patient. Three analysis methods are used to distribute a limited number of detections across the image set and further within each image, and additionally to perform a normalcy classification. The normalcy classification is used to remove all detections from an image set when predetermined normalcy conditions are met. The final output of the system is a set of indications overlaid on the input medical images.
    • 一种计算机辅助检测方法和系统,用于帮助放射科医师阅读医学图像。 该方法和系统在乳腺摄影领域具有特殊应用,包括检测聚类微钙化和密度。 提供了一种微钙化检测器,其中单独的检测是排序和分类的,并且使用多层感知器导出用于分类的特征之一。 提供了一种密度检测器,其包括具有嵌入式子系统的迭代动态区域增长模块,用于对候选掩模的最佳子集进行排序和分类。 提供后处理阶段,其中在用于患者的一组图像的上下文中分析检测。 三种分析方法用于跨越图像集并且进一步在每个图像内分布有限数量的检测,并且另外执行正常分类。 当满足预定的正常条件时,正常分类用于从图像集中去除所有检测。 系统的最终输出是覆盖在输入医学图像上的一组指示。
    • 5. 发明授权
    • Method for determining features from detections in a digital image using a bauer-fisher ratio
    • 一种使用鲍尔 - 比勒比法从数字图像检测中确定特征的方法
    • US06757415B1
    • 2004-06-29
    • US10461301
    • 2003-06-13
    • Steven K. RogersKenneth W. BauerMichael J. CollinsMartin P. DeSimioRichard A. Mitchell
    • Steven K. RogersKenneth W. BauerMichael J. CollinsMartin P. DeSimioRichard A. Mitchell
    • G06K900
    • G06K9/6293G06K9/4609G06K9/56G06K2209/05G06T7/0012G06T2207/30068
    • A computer aided detection method and system to assist radiologists in the reading of medical images. The method and system has particular application to the area of mammography including detection of clustered microcalcifications and densities. A microcalcification detector is provided wherein individual detections are rank ordered and classified, and one of the features for classification is derived using a multilayer perceptron. A density detector is provided including an iterative, dynamic region growing module with embedded subsystem for rank ordering and classification of a best subset of candidate masks. Features are computed from a detection on an input image by providing first and second regions on the input image corresponding to areas inside and outside the detection, measurements are computed based on values derived from the two regions, a standard deviation is computed for the measurements in each region, and a feature for the detection is computed using a Bauer-Fisher ratio. A post processing stage is provided where detections are analyzed in the context of a set of images for a patient. The final output of the system is a set of indications overlaid on the input medical images.
    • 一种计算机辅助检测方法和系统,用于帮助放射科医师阅读医学图像。 该方法和系统在乳腺摄影领域具有特殊应用,包括检测聚类微钙化和密度。 提供了一种微钙化检测器,其中单独的检测是排序和分类的,并且使用多层感知器导出用于分类的特征之一。 提供了一种密度检测器,其包括具有嵌入式子系统的迭代动态区域增长模块,用于对候选掩模的最佳子集进行排序和分类。 通过在对应于检测内部和外部的区域的输入图像上提供第一和第二区域来对输入图像上的检测计算特征,根据从两个区域导出的值来计算测量值,计算标准偏差 每个区域和用于检测的特征使用鲍尔 - 费雪比来计算。 提供后处理阶段,其中在用于患者的一组图像的上下文中分析检测。 系统的最终输出是覆盖在输入医学图像上的一组指示。
    • 9. 发明申请
    • COMPUTER-AIDED DETECTION AND CLASSIFICATION OF SUSPICIOUS MASSES IN BREAST IMAGERY
    • 乳腺成像中计算机辅助检测和分类的可疑质量
    • US20100067754A1
    • 2010-03-18
    • US12211593
    • 2008-09-16
    • Michael J. CollinsKevin WoodsBrent WoodsWilliam PiersonRyan McGinnis
    • Michael J. CollinsKevin WoodsBrent WoodsWilliam PiersonRyan McGinnis
    • G06K9/00G06K9/62
    • G06K9/6292G06K2209/053G06T7/0012G06T2207/30068
    • Methods, a system, and a computer readable medium are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In accordance with certain embodiments, for each suspicious mass located, a quantitative measure of spiculation and quantitative measure of density are computed. At least one classification scheme is then selected for each suspicious mass according to both quantitative measures. Each classification scheme is developed using true positives and false positives with similar quantitative measures.In accordance with certain other embodiments, for each suspicious mass located, a measure of breast location is computed. At least one classification scheme is then selected for each suspicious mass according to the measure of breast location. Each classification scheme is developed using true positives and false positives that appear in the same breast location. In one embodiment, the location measure determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.
    • 提出了方法,系统和计算机可读介质,其以高灵敏度和可接受的假阳性率检测和分类显示具有螺旋和/或密度特征的质量样区域。 在乳房的医学图像中识别出一个或多个可疑群体。 根据某些实施例,对于每个可疑的质量位置,计算密度的定量测量和密度的定量测量。 然后根据这两种量化措施,为每个可疑物质选择至少一种分类方案。 每个分类方案是使用具有相似量化措施的真阳性和假阳性来开发的。 根据某些其他实施例,对于位于每个可疑质量块,计算乳房位置的量度。 然后根据乳房位置的测量,为每个可疑质量选择至少一个分类方案。 每个分类方案使用出现在同一乳房位置的真阳性和假阳性进行开发。 在一个实施例中,位置测量确定可疑质量是否出现在乳房的实质区域的内部或外部。