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1. 发明授权

US5537485A Method for computer-aided detection of clustered microcalcifications from digital mammograms 失效
标题翻译：计算机辅助检测数字乳腺X线照片的聚类微钙化方法
公开(公告)号：US5537485A
公开(公告)日：1996-07-16
申请号：US915631
申请日：1992-07-21
申请人： Robert M. Nishikawa , Maryellen L. Giger , Kunio Doi
发明人： Robert M. Nishikawa , Maryellen L. Giger , Kunio Doi
IPC分类号： A61B6/00 , G06T7/00 , G06K9/00
CPC分类号： G06T7/0012 , G06T2207/30068
摘要： A method of computerized detection of clustered microcalcifications in digital mammograms, including obtaining a digitized mammogram, deriving a difference image signal from the digitized mammogram, performing global grey-level thresholding, area filtering, and local grey-level thresholding on the difference image, in that order, performing a texture discrimination of the signal extracted from the previous step, performing a cluster filtering technique on the texture discriminated signals to identify locations in the digitized mammogram corresponding to candidate clustered microcalcifications, performing a feature extraction step in which the area, contrast and background pixel values of signals corresponding to the candidate clustered microcalcifications in the original image are extracted, performing thresholding tests based on the extracted features to eliminate from the candidate clustered microcalcifications particular candidate clustered microcalcification identified as corresponding to false-positive identifications, cluster filtering the remaining candidate clustered microcalcifications to eliminate further candidate clustered microcalcifications which are not sufficiently clustered, and outputting to a radiologist an image indicating, by use of arrows, the positions of the remaining clustered microcalcifications.
摘要翻译：一种在数字乳腺X线照片中计算机化检测聚类微钙化的方法，包括获得数字化乳腺X线照片，从数字化乳房X光检查图导出差分图像信号，对差异图像执行全局灰度阈值处理，区域滤波和局部灰度阈值处理，执行从前一步骤提取的信号的纹理鉴别，对纹理鉴别信号执行群集滤波技术以识别对应于候选聚类微钙化的数字化乳房X线照片中的位置，执行特征提取步骤，其中区域，对比度并且提取与原始图像中的候选聚类微量化相对应的信号的背景像素值，基于所提取的特征进行阈值测试，以从候选聚类微量化中消除被识别为对应的特定候选聚类微钙化对于假阳性识别，聚类过滤剩余的候选聚类微钙化以消除未充分聚集的进一步的候选聚类微钙化，并且向放射科医师输出使用箭头指示剩余聚类微钙化位置的图像。

2. 发明授权

US07418123B2 Automated method and system for computerized image analysis for prognosis 有权
标题翻译：计算机图像分析自动化方法和系统预后
公开(公告)号：US07418123B2
公开(公告)日：2008-08-26
申请号：US10617675
申请日：2003-07-14
申请人： Maryellen L. Giger , Ioana Bonta , Ruth Heimann , Robert M. Nishikawa , Carl J. Vyborny
发明人： Maryellen L. Giger , Ioana Bonta , Ruth Heimann , Robert M. Nishikawa , Carl J. Vyborny
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/187 , G06T2207/10088 , G06T2207/10132 , G06T2207/30068 , G06T2207/30096
摘要： An automated method for determining prognosis based on an analysis of abnormality (lesion) features and parenchymal features obtained from medical image data of a patient. The techniques include segmentation of lesions from radiographic images, extraction of lesion features, and a merging of the features (with and without clinical information) to yield as estimate of the prognosis for the specific case. An example is given for the prognosis of breast cancer lesions using mammographic data. A computerized image analysis system for assessing prognosis combines the computerized analysis of medical images of cancerous lesions with the training-based methods of assessing prognosis of a patient, using indicators such as lymph node involvement, presence of metastatic disease, local recurrence, and/or death. It is expected that use of such a system to assess the severity of the disease will aid in improved decision-making regarding treatment options.
摘要翻译：一种用于基于从患者的医学图像数据获得的异常（损伤）特征和实质特征的分析来确定预后的自动化方法。这些技术包括从放射照相图像分割病变，提取病变特征，以及特征（有和没有临床信息）的合并，以产生具体病例的预后估计。使用乳房X线摄影数据给出乳腺癌病变预后的一个例子。用于评估预后的计算机化图像分析系统将癌症病变的医学图像的计算机化分析与使用诸如淋巴结受累，转移性疾病的存在，局部复发的指标以及/或患者的评估患者预后的基于训练的方法相结合死亡。预计使用这样的系统来评估疾病的严重程度将有助于改善治疗方案的决策。

3. 发明授权

US5666434A Computer-aided method for image feature analysis and diagnosis in mammography 失效
公开(公告)号：US5666434A
公开(公告)日：1997-09-09
申请号：US536794
申请日：1995-09-29
申请人： Robert M. Nishikawa , Takehiro Ema , Hiroyuki Yoshida , Kunio Doi
发明人： Robert M. Nishikawa , Takehiro Ema , Hiroyuki Yoshida , Kunio Doi
IPC分类号： A61B6/00 , G06F19/00 , G06K9/52 , G06T7/00 , G06K9/00
CPC分类号： G06T7/0012 , A61B6/5258 , G06K9/527 , G06T2207/10116 , G06T2207/20064 , G06T2207/30068 , Y10S128/925
摘要： A method for automated detection of abnormal anatomic regions, wherein a mammogram is digitized to produce a digital image and the digital image is processed using local edge gradient analysis and linear pattern analysis in addition to feature extraction routines to identify abnormal anatomic regions. Noise reduction filtering and pit-filling/spike-removal filtering techniques are also provided. Multiple difference imaging techniques are also used in which difference images employing different filter characteristics are obtained and processing results logically OR'ed to identify abnormal anatomic regions. In another embodiment the processing results with and without noise reduction filtering are logically AND'ed to improve detection sensitivity. Also, in another embodiment the wavelet transform is utilized in the identification and detection of abnormal regions. The wavelet transform is preferably used in conjunction with the difference imaging technique with the results of the two techniques being logically OR'ed.

4. 发明授权

US6075878A Method for determining an optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms 失效
标题翻译：基于用于检测数字乳腺X线照片中微钙化的监督训练来确定最佳加权小波变换的方法
公开(公告)号：US6075878A
公开(公告)日：2000-06-13
申请号：US979623
申请日：1997-11-28
申请人： Hiroyuki Yoshida , Wei Zhang , Robert M. Nishikawa , Kunio Doi
发明人： Hiroyuki Yoshida , Wei Zhang , Robert M. Nishikawa , Kunio Doi
IPC分类号： G06F17/14 , G06T7/00 , G06K9/00
CPC分类号： G06F17/148 , G06T7/0012
摘要： A computer-aided diagnosis (CAD) method for detection of clustered microcalcifications in digital mammograms based on an image reconstruction using a substantially optimally weighted wavelet transform. Weights at individual scales of the wavelet transform are optimized based on a supervised learning method. In the learning method, an error function represents a difference between a desired output and a reconstructed image obtained from weighted wavelet coefficients of the wavelet transform for a given mammogram. The error function is then minimized by modifying the weights by means of a conjugate gradient algorithm. Performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis which indicated that the present invention outperformed both a difference-image technique and partial reconstruction method currently used in CAD methods.
摘要翻译：一种用于基于使用基本上最佳加权的小波变换的图像重建来检测数字乳腺X线照片中的聚类微钙化的计算机辅助诊断（CAD）方法。基于监督学习方法优化小波变换的个体尺度权重。在学习方法中，误差函数表示对于给定的乳腺X线照片，从小波变换的加权小波系数获得的期望输出和重建图像之间的差。然后通过使用共轭梯度算法修改权重来最小化误差函数。通过接收机操作特性（ROC）分析评估了最佳加权小波的性能，表明本发明优于当前在CAD方法中使用的差分图像技术和部分重建方法。

5. 发明授权

US6058322A Methods for improving the accuracy in differential diagnosis on radiologic examinations 失效
公开(公告)号：US6058322A
公开(公告)日：2000-05-02
申请号：US900361
申请日：1997-07-25
申请人： Robert M. Nishikawa , Yulei Jiang , Kazuto Ashizawa , Kunio Doi
发明人： Robert M. Nishikawa , Yulei Jiang , Kazuto Ashizawa , Kunio Doi
IPC分类号： A61B6/00 , G06T1/00 , G06T7/00 , A61B5/05
CPC分类号： G06T7/0012 , Y10S128/925
摘要： A computer-aided method for detecting, classifying, and displaying candidate abnormalities, such as microcalcifications and interstitial lung disease in digitized medical images, such as mammograms and chest radiographs, a computer programmed to implement the method, and a data structure for storing required parameters, wherein in the classifying method candidate abnormalities in a digitized medical image are located, regions are generated around one or more of the located candidate abnormalities, features are extracted from at least one of the located candidate abnormalities within the region and from the region itself, the extracted features are applied to a classification technique, such as an artificial neural network (ANN) to produce a classification result (i.e., probability of malignancy in the form of a number and a bar graph), and the classification result is displayed along with the digitized medical image annotated with the region and the candidate abnormalities within the region. In the detecting method candidate abnormalities in each of a plurality of digitized medical images are located, regions around one or more of the located candidate abnormalities in each of a plurality of digitized medical images are generated, the plurality of digitized medical images annotated with respective regions and candidate abnormalities within the regions are displayed, and a first indicator (e.g., blue arrow) is superimposed over candidate abnormalities comprising of clusters and a second indicator (e.g., red arrow) is superimposed over candidate abnormalities comprising of masses. In a user modification mode, during classification, a user modifies the located candidate abnormalities, the determined regions, and/or the extracted features, so as to modify the extracted features applied to the classification technique and the displayed results, and, during detection, a user modifies the located candidate abnormalities, the determined regions, and the extracted features, so as to modify the displayed results.

6. 发明授权

US5740268A Computer-aided method for image feature analysis and diagnosis in mammography 失效
标题翻译：计算机辅助方法用于乳腺摄影术中的图像特征分析和诊断
公开(公告)号：US5740268A
公开(公告)日：1998-04-14
申请号：US536253
申请日：1995-09-29
申请人： Robert M. Nishikawa , Takehiro Ema , Hiroyuki Yoshida , Kunio Doi
发明人： Robert M. Nishikawa , Takehiro Ema , Hiroyuki Yoshida , Kunio Doi
IPC分类号： A61B6/00 , G06F19/00 , G06K9/52 , G06T7/00 , G06K9/00
CPC分类号： G06T7/0012 , A61B6/5258 , G06K9/527 , G06T2207/10116 , G06T2207/20064 , G06T2207/30068 , Y10S128/925
摘要： A method for automated detection of abnormal anatomic regions, wherein a mammogram is digitized to produce a digital image and the digital image is processed using local edge gradient analysis and linear pattern analysis in addition to feature extraction routines to identify abnormal anatomic regions. Noise reduction filtering and pit-filling/spike-removal filtering techniques are also provided. Multiple difference imaging techniques are also used in which difference images employing different filter characteristics are obtained and processing results logically OR'ed to identify abnormal anatomic regions. In another embodiment the processing results with and without noise reduction filtering are logically AND'ed to improve detection sensitivity. Also, in another embodiment the wavelet transform is utilized in the identification and detection of abnormal regions. The wavelet transform is preferably used in conjunction with the difference imaging technique with the results of the two techniques being logically OR'ed.
摘要翻译：一种用于自动检测异常解剖区域的方法，其中乳房X线照片被数字化以产生数字图像，并且除了特征提取例程以识别异常解剖区域之外，还使用局部边缘梯度分析和线性模式分析处理数字图像。还提供降噪滤波和凹坑填充/尖峰去除滤波技术。还使用多重差分成像技术，其中获得采用不同滤光器特征的差异图像，并且逻辑上OR'的处理结果用于识别异常解剖区域。在另一个实施例中，具有和不具有噪声降低滤波的处理结果被逻辑地“和”以提高检测灵敏度。此外，在另一实施例中，小波变换用于异常区域的识别和检测。小波变换优选与差分成像技术结合使用，两种技术的结果在逻辑上是有逻辑关系的。

7. 发明授权

US5673332A Computer-aided method for image feature analysis and diagnosis in mammography 失效
标题翻译：计算机辅助方法用于乳腺摄影术中的图像特征分析和诊断
公开(公告)号：US5673332A
公开(公告)日：1997-09-30
申请号：US693502
申请日：1996-08-08
申请人： Robert M. Nishikawa , Takehiro Ema , Hiroyuki Yoshida , Kunio Doi
发明人： Robert M. Nishikawa , Takehiro Ema , Hiroyuki Yoshida , Kunio Doi
IPC分类号： A61B6/00 , G06F19/00 , G06K9/52 , G06T7/00 , G06K9/00
CPC分类号： G06T7/0012 , A61B6/5258 , G06K9/527 , G06T2207/10116 , G06T2207/20064 , G06T2207/30068 , Y10S128/925
摘要： A method for automated detection of abnormal anatomic regions, wherein a mammogram is digitized to produce a digital image and the digital image is processed using local edge gradient analysis and linear pattern analysis in addition to feature extraction routines to identify abnormal anatomic regions. Noise reduction filtering and pit-filling/spike-removal filtering techniques are also provided. Multiple difference imaging techniques are also used in which difference images employing different filter characteristics are obtained and processing results logically OR'ed to identify abnormal anatomic regions. In another embodiment the processing results with and without noise reduction filtering are logically AND'ed to improve detection sensitivity. Also, in another embodiment the wavelet transform is utilized in the identification and detection of abnormal regions. The wavelet transform is preferably used in conjunction with the difference imaging technique with the results of the two techniques being logically OR'ed.
摘要翻译：用于自动检测异常解剖区域的方法，其中乳房X线照片被数字化以产生数字图像，并且除了特征提取例程以识别异常解剖区域之外，还使用局部边缘梯度分析和线性模式分析处理数字图像。还提供降噪滤波和凹坑填充/尖峰去除滤波技术。还使用多重差分成像技术，其中获得采用不同滤光器特征的差异图像，并且逻辑上OR'的处理结果用于识别异常解剖区域。在另一个实施例中，具有和不具有噪声降低滤波的处理结果被逻辑地“和”以提高检测灵敏度。此外，在另一实施例中，小波变换用于异常区域的识别和检测。小波变换优选与差分成像技术结合使用，两种技术的结果在逻辑上是有逻辑关系的。

8. 发明授权

US5598481A Computer-aided method for image feature analysis and diagnosis in mammography 失效
公开(公告)号：US5598481A
公开(公告)日：1997-01-28
申请号：US536450
申请日：1995-09-29
申请人： Robert M. Nishikawa , Takehiro Ema , Hiroyuki Yoshida , Kunio Doi
发明人： Robert M. Nishikawa , Takehiro Ema , Hiroyuki Yoshida , Kunio Doi
IPC分类号： A61B6/00 , G06F19/00 , G06K9/52 , G06T7/00 , G06K9/00
CPC分类号： G06T7/0012 , A61B6/5258 , G06K9/527 , G06T2207/10116 , G06T2207/20064 , G06T2207/30068 , Y10S128/925
摘要： A method for automated detection of abnormal anatomic regions, wherein a mammogram is digitized to produce a digital image and the digital image is processed using local edge gradient analysis and linear pattern analysis in addition to feature extraction routines to identify abnormal anatomic regions. Noise reduction filtering and pit-filling/spike-removal filtering techniques are also provided. Multiple difference imaging techniques are also used in which difference images employing different filter characteristics are obtained and processing results logically OR'ed to identify abnormal anatomic regions. In another embodiment the processing results with and without noise reduction filtering are logically AND'ed to improve detection sensitivity. Also, in another embodiment the wavelet transform is utilized in the identification and detection of abnormal regions. The wavelet transform is preferably used in conjunction with the difference imaging technique with the results of the two techniques being logically OR'ed.

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