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1. 发明申请

WO2008030247A3 METHOD FOR DETECTION OF VERTEBRAL FRACTURES ON LATERAL CHEST RADIOGRAPHS 审中-公开
标题翻译：方法检测横向胸部放射学上的椎体骨折
公开(公告)号：WO2008030247A3
公开(公告)日：2008-08-28
申请号：PCT/US2006036516
申请日：2006-09-19
申请人： DOI KUNIO , KASAI SATOSHI
发明人： DOI KUNIO , KASAI SATOSHI
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , A61B5/1075 , A61B5/4504 , A61B6/505 , G06T7/12 , G06T7/62 , G06T2207/10116 , G06T2207/30012
摘要： A method, system, and computer program product for detecting vertebral fractures, including steps of (1) obtaining a medical image including a plurality of vertebrae; (2) detecting, corresponding edges of the plurality of vertebra using line enhancement and feature analysis; (3) determining the vertebral height of each vertebra based on a location of the detected edges of the vertebra; and (4) analyzing the determined vertebral heights to identify fractured vertebra.
摘要翻译：一种用于检测椎骨骨折的方法，系统和计算机程序产品，包括以下步骤：（1）获得包括多个椎骨的医学图像; （2）使用线增强和特征分析检测多个椎骨的对应边缘; （3）基于检测到的椎骨边缘的位置确定每个椎骨的椎骨高度; 和（4）分析确定的椎骨高度以识别骨折椎骨。

2. 发明申请

WO2006093523A2 COMPUTERIZED SCHEME FOR DISTINCTION BETWEEN BENIGN AND MALIGNANT NODULES IN THORACIC LOW-DOSE CT 审中-公开
标题翻译：计算方法在胸腔低通量CT中的定位和恶性肿瘤之间的分类
公开(公告)号：WO2006093523A2
公开(公告)日：2006-09-08
申请号：PCT/US2005/025305
申请日：2005-07-15
申请人： SUZUKI, Kenji , DOI, Kunio
发明人： SUZUKI, Kenji , DOI, Kunio
CPC分类号： G06K9/3233 , G06K9/6292 , G06K2209/05 , G06T7/0012 , G06T2207/30061
摘要： A system, method, and computer program product for classifying a target structure in an image into abnormality types. The system has a scanning mechanism that scans a local window across sub-regions of the target structure by moving the local window across the image to obtain sub-region pixel sets. A mechanism inputs the sub-region pixel sets into a classifier to provide output pixel values based on the sub-region pixel sets, each output pixel value representing a likelihood that respective image pixels have a predetermined abnormality, the output pixel values collectively determining a likelihood distribution output image map. A mechanism scores the likelihood distribution map to classify the target structure into abnormality types. The classifier can be, e.g., a single-output or multiple-output massive training artificial neural network (MTANN).
摘要翻译：一种用于将图像中的目标结构分类为异常类型的系统，方法和计算机程序产品。该系统具有扫描机构，通过移动该图像上的局部窗口来扫描目标结构的子区域上的局部窗口，以获得子区域像素集。机构将子区域像素集合输入到分类器中以基于子区域像素集提供输出像素值，每个输出像素值表示各个图像像素具有预定异常的可能性，输出像素值共同确定似然分布输出图像映射。一种机制评估了可能性分布图，将目标结构分类为异常类型。分类器可以是例如单输出或多输出大规模训练人造神经网络（MTANN）。

3. 发明申请

WO2008030247B1 METHOD FOR DETECTION OF VERTEBRAL FRACTURES ON LATERAL CHEST RADIOGRAPHS 审中-公开
标题翻译：用于检测侧向切片的VERTEBRAL FRACTURES的方法
公开(公告)号：WO2008030247B1
公开(公告)日：2008-10-23
申请号：PCT/US2006036516
申请日：2006-09-19
申请人： DOI KUNIO , KASAI SATOSHI
发明人： DOI KUNIO , KASAI SATOSHI
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , A61B5/1075 , A61B5/4504 , A61B6/505 , G06T7/12 , G06T7/62 , G06T2207/10116 , G06T2207/30012
摘要： A method, system, and computer program product for detecting vertebral fractures, including steps of (1) obtaining a medical image including a plurality of vertebrae; (2) detecting, corresponding edges of the plurality of vertebra using line enhancement and feature analysis; (3) determining the vertebral height of each vertebra based on a location of the detected edges of the vertebra; and (4) analyzing the determined vertebral heights to identify fractured vertebra.
摘要翻译：一种用于检测椎骨骨折的方法，系统和计算机程序产品，包括（1）获得包括多个椎骨的医学图像的步骤; （2）使用线条增强和特征分析检测多个椎骨的相应边缘; （3）基于所检测的椎骨边缘的位置来确定每个椎骨的椎体高度; 和（4）分析确定的椎体高度以识别骨折的椎骨。

4. 发明申请

WO2006093523A3 COMPUTERIZED SCHEME FOR DISTINCTION BETWEEN BENIGN AND MALIGNANT NODULES IN THORACIC LOW-DOSE CT 审中-公开
标题翻译：计算方法在胸腔低通量CT中的定位和恶性肿瘤之间的分类
公开(公告)号：WO2006093523A3
公开(公告)日：2007-02-01
申请号：PCT/US2005025305
申请日：2005-07-15
申请人： SUZUKI KENJI , DOI KUNIO
发明人： SUZUKI KENJI , DOI KUNIO
IPC分类号： G06K9/62
CPC分类号： G06K9/3233 , G06K9/6292 , G06K2209/05 , G06T7/0012 , G06T2207/30061
摘要： A system, method, and computer program product for classifying a target structure in an image into abnormality types. The system has a scanning mechanism that scans a local window across sub-regions of the target structure by moving the local window across the image to obtain sub-region pixel sets (fig. 2b element 200). A mechanism inputs the sub-region pixel sets into a classifier (fig. 2b element 210) to provide output pixel values based on the sub-region pixel sets, each output pixel abnormality, the output pixel values collectively determining a likelihood distribution output image map. A mechanism scores the likelihood distribution map to classify the target structure into abnormality types (fig. 2b element 220). The classifier cab be, e.g. , a single-output or multiple-output massive trainin artifical neural network MTANN .
摘要翻译：一种用于将图像中的目标结构分类为异常类型的系统，方法和计算机程序产品。该系统具有扫描机构，通过在该图像上移动局部窗口来扫描目标结构的子区域上的局部窗口，以获得子区域像素集（图2b元素200）。机构将子区域像素集合输入到分类器（图2b元件210）中，以基于子区域像素集合提供输出像素值，每个输出像素异常，输出像素值共同确定似然分布输出图像映射。机制评估似然分布图，将目标结构分类为异常类型（图2b元素220）。分类器驾驶室是例如。，单输出或多输出大量训练人造神经网络MTANN。

5. 发明申请

WO2004074982A3 METHOD OF TRAINING MASSIVE TRAINING ARTIFICIAL NEURAL NETWORKS (MTANN) FOR THE DETECTION OF ABNORMALITIES IN MEDICAL IMAGES 审中-公开
公开(公告)号：WO2004074982A3
公开(公告)日：2004-09-02
申请号：PCT/US2004/002018
申请日：2004-02-12
申请人： THE UNIVERSITY OF CHICAGO , SUZUKI, Kenji , DOI, Kunio
发明人： SUZUKI, Kenji , DOI, Kunio
IPC分类号： G06K9/62
摘要： A method, system, and computer program product of selecting a set of training images for a massive training artificial neural network (MTANN). The method comprises selecting the set of training images from a set of domain images; training the MTANN with the set of training images; applying a plurality of images from the set of domain images to the trained MTANN to obtain a corresponding plurality of scores; and determining the set of training images based on the plurality of images, the corresponding plurality of scores, and the set of training images. The method is useful for the reduction of false positives in computerized detection of abnormalities in medical images. In particular, the MTAAN can be used for the detection of lung nodules in low-dose CT (LDCT). The MTANN consists of a modified multilayer artificial neural network capable of operating on image data directly.

6. 发明申请

WO2004070648A1 A METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR COMPUTER-AIDED DETECTION OF NODULES WITH THREE DIMENSIONAL SHAPE ENHANCEMENT FILTERS 审中-公开
标题翻译：用于计算机辅助检测具有三维形状增强滤波器的方法，系统和计算机程序产品
公开(公告)号：WO2004070648A1
公开(公告)日：2004-08-19
申请号：PCT/US2003/032740
申请日：2003-11-06
申请人： THE UNIVERSITY OF CHICAGO , LI, Qiang , DOI, Kunio
发明人： LI, Qiang , DOI, Kunio
IPC分类号： G06K9/00
CPC分类号： G06K9/527 , G06T5/002 , G06T5/003 , G06T5/20 , G06T7/0012 , G06T7/11 , G06T2207/10081 , G06T2207/20132 , G06T2207/30064
摘要： A method, system, and computer program product for evaluating an image including an object (S2) including filtering (S3) image data derived from the image with a first geometric enhancement filter (S6) having magnitude and likelihood filter components so as to produce first filtered image data in which a first geometric pattern is enhanced.
摘要翻译：一种用于评估包括物体（S2）的图像的方法，系统和计算机程序产品，包括用具有幅度和似然滤波器分量的第一几何增强滤波器（S6）滤波（S3）从图像导出的图像数据，以产生第一其中增强了第一几何图案的滤波图像数据。

7. 发明申请

WO0030021A9 SYSTEM FOR DETECTION OF MALIGNANCY IN PULMONARY NODULES 审中-公开
标题翻译：系统检测肺部恶性程度
公开(公告)号：WO0030021A9
公开(公告)日：2000-10-19
申请号：PCT/US9925998
申请日：1999-11-12
申请人： ARCH DEV CORP , DOI KUNIO , NAKAMURA KATSUMI
发明人： DOI KUNIO , NAKAMURA KATSUMI
IPC分类号： A61B6/03 , A61B6/00 , G06K9/00 , G06T1/00 , G06T7/00
CPC分类号： G06T7/0012 , G06K9/00127
摘要： A method, computer program product, and system (100) for computerized analysis of the likelihood of malignancy in a pulmonary nodule using artificial neural networks (ANNs) (S4). The method, on which the computer program product and the system is based on, includes obtaining a digital outline of a nodule; generating objective measures corresponding to physical features of the outline of the nodule; applying the generated objective measures to an ANN; and determining a likelihood of malignancy of the nodule based on an output of the ANN. Techniques include novel developments and implementations of artificial neural networks and feature extraction for digital images. Output from the inventive method yields an estimate of the likelihood of malignancy (S7) for a pulmonary nodule.
摘要翻译：一种使用人工神经网络（ANN）（S4）对肺结节中的恶性可能性进行计算机化分析的方法，计算机程序产品和系统（100）。计算机程序产品和系统所基于的方法包括获得结节的数字轮廓; 产生与结节轮廓的物理特征相对应的客观量度; 将所产生的客观措施应用于ANN; 以及基于ANN的输出确定结节恶性的可能性。技术包括人工神经网络的新颖开发和实现以及数字图像的特征提取。本发明方法的输出产生肺结节恶性可能性的估计（S7）。

8. 发明申请

WO2008030247A2 METHOD FOR DETECTION OF VERTEBRAL FRACTURES ON LATERAL CHEST RADIOGRAPHS 审中-公开
标题翻译：方法检测横向胸部放射学上的椎体骨折
公开(公告)号：WO2008030247A2
公开(公告)日：2008-03-13
申请号：PCT/US2006/036516
申请日：2006-09-19
申请人： DOI, Kunio , KASAI, Satoshi
发明人： DOI, Kunio , KASAI, Satoshi
IPC分类号： G06T7/40
CPC分类号： G06T7/0012 , A61B5/1075 , A61B5/4504 , A61B6/505 , G06T7/12 , G06T7/62 , G06T2207/10116 , G06T2207/30012
摘要： A method, system, and computer program product for detecting vertebral fractures, including steps of (1) obtaining a medical image including a plurality of vertebrae; (2) detecting, corresponding edges of the plurality of vertebra using line enhancement and feature analysis; (3) determining the vertebral height of each vertebra based on a location of the detected edges of the vertebra; and (4) analyzing the determined vertebral heights to identify fractured vertebra.
摘要翻译：一种用于检测椎骨骨折的方法，系统和计算机程序产品，包括以下步骤：（1）获得包括多个椎骨的医学图像; （2）使用线增强和特征分析检测多个椎骨的对应边缘; （3）基于检测到的椎骨边缘的位置确定每个椎骨的椎骨高度; 和（4）分析确定的椎骨高度以识别骨折的椎骨。

9. 发明申请

WO2004074982A2 METHOD OF TRAINING MASSIVE TRAINING ARTIFICIAL NEURAL NETWORKS (MTANN) FOR THE DETECTION OF ABNORMALITIES IN MEDICAL IMAGES 审中-公开
标题翻译：训练用于检测医学图像异常的大规模训练人工神经网络（MTANN）的方法
公开(公告)号：WO2004074982A2
公开(公告)日：2004-09-02
申请号：PCT/US2004002018
申请日：2004-02-12
申请人： UNIV CHICAGO , SUZUKI KENJI , DOI KUNIO
发明人： SUZUKI KENJI , DOI KUNIO
IPC分类号： G06K9/32 , G06K9/62 , G06N3/08 , G06T7/00 , G06F
CPC分类号： G06K9/32 , G06K9/6256 , G06N3/08 , G06T7/0012 , G06T2207/30004
摘要： A method, system, and computer program product of selecting a set of training images for a massive training artificial neural network (MTANN). The method comprises selecting the set of training images from a set of domain images; training the MTANN with the set of training images; applying a plurality of images from the set of domain images to the trained MTANN to obtain a corresponding plurality of scores; and determining the set of training images based on the plurality of images, the corresponding plurality of scores, and the set of training images. The method is useful for the reduction of false positives in computerized detection of abnormalities in medical images. In particular, the MTAAN can be used for the detection of lung nodules in low-dose CT (LDCT). The MTANN consists of a modified multilayer artificial neural network capable of operating on image data directly.
摘要翻译：一种为大规模训练人造神经网络（MTANN）选择一组训练图像的方法，系统和计算机程序产品。该方法包括从一组域图像中选择一组训练图像; 训练MTANN与一套训练图像; 将来自所述一组域图像的多个图像应用于所训练的MTANN以获得相应的多个分数; 以及基于所述多个图像，所述相应的多个分数以及所述一组训练图像来确定所述训练图像的集合。该方法可用于减少计算机化检测医学图像异常的假阳性。特别地，MTAAN可用于检测低剂量CT（LDCT）中的肺结节。 MTANN由能够直接对图像数据进行操作的改进的多层人造神经网络组成。

10. 发明申请

WO2003087983A3 MASSIVE TRAINING ARTIFICIAL NEURAL NETWORK (MTANN) FOR DETECTING ABNORMALITIES IN MEDICAL IMAGES 审中-公开
公开(公告)号：WO2003087983A3
公开(公告)日：2003-10-23
申请号：PCT/US2003/010468
申请日：2003-04-14
申请人： THE UNIVERSITY OF CHICAGO , SUZUKI, Kenji , DOI, Kunio
发明人： SUZUKI, Kenji , DOI, Kunio
IPC分类号： G06K9/62
摘要： A method of training an artificial neural network (ANN) involves receiving a likelihood distribution map as a teacher image, receiving a training image, moving a local window across sub-regions of the training image to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to the ANN so that it provides output pixel values that are compared to output pixel values of corresponding teacher image pixel values to determine an error, and training the ANN to reduce the error. A method of detecting a target structure in an image involves scanning a local window across sub-regions of the image by moving the local window for each sub-region so as to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to an ANN so that it provides respective output pixel values that represent likelihoods that respective image pixels are part of a target structure, the output pixel values collectively constituting a likelihood distribution map. Another method for detecting a target structure involves training N parallel ANNs on either (A) a same target structure and N mutually different non-target structures, or (B) a same non-target structure and N mutually different target structures, the ANNs outputting N respective indications of whether the image includes a target structure or a non-target structure, and combining the N indications to form a combined indication of whether the image includes a target structure or a non-target structure. The invention provides related apparatus and computer program products storing executable instructions to perform the methods.

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