公开(公告)号：US5931780A

公开(公告)日：1999-08-03

申请号：US158388

申请日：1993-11-29

申请人： Maryellen L. Giger ,  Kunio Doi

发明人： Maryellen L. Giger ,  Kunio Doi

IPC分类号： A61B6/00 ,  G06F19/00 ,  G06T7/00 ,  A61B5/05

CPC分类号： G06T7/0012 ,  A61B6/482 ,  A61B6/505 ,  A61B6/583 ,  A61B6/4042 ,  G06T2207/10116 ,  G06T2207/30008 ,  G06T2207/30012

摘要： A computerized method and system for the radiographic analysis of bone structure and risk of future fracture with or without the measurement of bone mass. Techniques including texture analysis for use in quantitating the bone structure and risk of future fracture. The texture analysis of the bone structure incorporates directionality information, for example in terms of the angular dependence of the RMS variation and first moment of the power spectrum of a ROI in the bony region of interest. The system also includes using dual energy imaging in order to obtain measures of both bone mass and bone structure with one exam. Specific applications are given for the analysis of regions within the vertebral bodies on conventional spine radiographs. Techniques include novel features that characterize the power spectrum of the bone structure and allow extraction of directionality features with which to characterize the spatial distribution and thickness of the bone trabeculae. These features are then merged using artificial neural networks in order to yield a likelihood of risk of future fracture. In addition, a method and system is presented in which dual-energy imaging techniques are used to yield measures of both bone mass and bone structure with one low-dose radiographic examination; thus, making the system desirable for screening (for osteoporosis and risk of future fracture).

摘要翻译： 一种计算机化方法和系统，用于骨骼结构的射线照相分析和未来骨折的风险，有或没有骨量的测量。 包括用于量化骨骼结构和未来骨折风险的纹理分析的技术。 骨结构的纹理分析包括方向性信息，例如在感兴趣的骨区域中的ROI的RMS变化和ROI的功率谱的第一时刻的角度依赖性方面。 该系统还包括使用双能量成像，以便通过一次检查获得骨量和骨骼结构的测量。 给出了常规脊柱X光照片对椎体内部区域进行分析的具体应用。 技术包括表征骨骼结构的功率谱的新特征，并且允许提取用于表征骨小梁的空间分布和厚度的方向性特征。 然后使用人工神经网络将这些特征合并，以产生未来骨折风险的可能性。 此外，提出了一种方法和系统，其中使用双能量成像技术通过一次低剂量射线照相检查来产生骨量和骨结构的测量; 因此，使得该系统对于筛选（对于骨质疏松症和未来骨折的风险）是理想的。

公开(公告)号：US5873824A

公开(公告)日：1999-02-23

申请号：US758438

申请日：1996-11-29

申请人： Kunio Doi ,  Takayuki Ishida ,  Shigehiki Katsuragwa

发明人： Kunio Doi ,  Takayuki Ishida ,  Shigehiki Katsuragwa

IPC分类号： A61B5/00 ,  G06F15/18 ,  G06N3/00 ,  G06Q50/00 ,  G06T1/00 ,  G06T7/00

CPC分类号： G06K9/00 ,  G06T7/0012 ,  G06K2209/05 ,  Y10S128/925

摘要： An automated computer-aided diagnosis (CAD) method and system using artificial neural networks (ANNs) for the quantitative analysis of image data. Three separate ANNs were applied for detection of interstitial disease on digitized two-dimensional chest images. The first ANN was trained with horizontal profiles in regions of interest (ROIs) selected from normal and abnormal chest radiographs. The second ANN was trained using vertical output patterns obtained from the 1.sup.st ANN for each ROI. The output value of the 2.sup.nd ANN was used to distinguish between normal and abnormal ROIS with interstitial infiltrates. If the ratio of the number of abnormal ROIs to the total number of all ROIs in a chest image was greater than a certain threshold level, the chest image was considered abnormal. In addition, the third ANN was applied to distinguish between normal and abnormal chest images where the chest image was not clearly normal or abnormal. The ANN trained with image data learns some statistical properties associated with interstitial infiltrates in chest radiographs. In addition, the same technique can be applied to higher-dimensional data (e.g., three-dimensional data and four-dimensional data including time-varying three-dimensional data).

摘要翻译： 一种使用人工神经网络（ANN）进行图像数据定量分析的自动化计算机辅助诊断（CAD）方法和系统。 应用三个独立的ANN来检测数字化二维胸部图像上的间质性疾病。 在正常和异常的胸片中选择感兴趣区域（ROI）中的第一个ANN进行了水平剖面的训练。 使用从每个投资回报率的第一ANN获得的垂直输出模式训练第二ANN。 第二ANN的输出值用于区分正常和异常的ROIS与间质浸润。 如果胸部图像中异常ROI的数量与所有ROI的总数之比大于某一阈值水平，则胸部图像被认为是异常的。 另外，第三个ANN应用于区分胸部图像不正常或异常的正常和异常胸部图像。 用图像数据训练的ANN学习了与胸部X光片中的间质浸润相关的一些统计特性。 此外，相同的技术可以应用于高维数据（例如，三维数据和包括时变三维数据的四维数据）。

公开(公告)号：US5072384A

公开(公告)日：1991-12-10

申请号：US275720

申请日：1988-11-23

申请人： Kunio Doi ,  Nobuyuki Nakamori

发明人： Kunio Doi ,  Nobuyuki Nakamori

IPC分类号： A61B6/00 ,  G06T1/00 ,  G06T5/00 ,  G06T7/60

CPC分类号： G06T7/0083 ,  G06T7/606 ,  G06T2207/10116 ,  G06T2207/20056 ,  G06T2207/30048 ,  G06T2207/30061

摘要： An automated method and system to determine a number of parameters related to the size and shape of the heart as well as parameters related to the lungs from data derived from digital chest radiographs. A cardiac rectangle enclosing the heart and portions of the surrounding lung tissue is determined, and within the cardiac rectangle, horizontal and vertical profiles, and the first derivatives thereof, are determined. Based on these derivatives, cardiac boundary points on the left and right sides of the cardiac contour are determined, as well as diaphragm edge points. A predetermined model function is then fitted to selected of the determined cardiac boundary points to determine the cardiac contour. Tests are performed to determine whether or not the heart has an abnormal size or is a "tall" heart, and if so, corrective measures are taken. In a preferred embodiment, a shift-variant cosine function is used as a model function fitted to the selected cardiac boundary points. In an alternative embodiment, the model function is equivalent to the partial summation of a Fourier series. In an alternative embodiment for determining cardiac boundaries, an analysis is made of edge gradients obtained in two orthogonal directions in plural narrow band regions of the data from the digital chest radiograph.

摘要翻译： 一种自动化方法和系统，用于根据从数字胸部X光照片得到的数据来确定与心脏的大小和形状有关的多个参数以及与肺有关的参数。 确定围绕心脏和周围肺组织的部分的心脏矩形，并且在心矩形内，确定水平和垂直剖面及其一阶导数。 基于这些衍生物，确定心脏轮廓左侧和右侧的心脏边界点以及膜片边缘点。 然后将预定的模型函数拟合到所选择的确定的心脏边界点以确定心脏轮廓。 执行测试以确定心脏是否具有异常大小或是“高”心脏，如果是，采取纠正措施。 在优选实施例中，使用移位变换余弦函数作为拟合到所选择的心脏边界点的模型函数。 在替代实施例中，模型函数等效于傅立叶级数的部分求和。 在用于确定心脏边界的替代实施例中，分析来自数字胸部X光片的数据的多个窄带区域中在两个正交方向上获得的边缘梯度。

公开(公告)号：US4907156A

公开(公告)日：1990-03-06

申请号：US68221

申请日：1987-06-30

申请人： Kunio Doi ,  Heang-Ping Chan ,  Maryellen L. Giger

发明人： Kunio Doi ,  Heang-Ping Chan ,  Maryellen L. Giger

IPC分类号： G06T5/50 ,  G06T7/00

CPC分类号： G06T5/004 ,  G06T7/0002 ,  G06T7/0012 ,  G06T2207/30064

摘要： A method and system for detecting and displaying abnormal anatomic regions existing in a digital X-ray image, wherein a single projection digital X-ray image is processed to obtain signal-enhanced image data with a maximum signal-to-noise ratio (SNR) and is also processed to obtain signal-suppressed image data with a suppressed SNR. Then, difference image data are formed by subtraction of the signal-suppressed image data from the signal-enhanced image data to remove low-frequency structured anatomic background, which is basically the same in both the signal-suppressed and signal-enhanced image data. Once the structured background is removed, feature extraction, is performed. For the detection of lung nodules, pixel thresholding is performed, followed by circularity and/or size testing of contiguous pixels surviving thresholding. Threshold levels are varied, and the effect of varying the threshold on circularity and size is used to detect nodules. For the detection of mammographic microcalcifications, pixel thresholding and contiguous pixel area thresholding are performed. Clusters of suspected abnormalities are then detected.

摘要翻译： 一种用于检测和显示存在于数字X射线图像中的异常解剖区域的方法和系统，其中处理单个投影数字X射线图像以获得具有最大信噪比（SNR）的信号增强图像数据， 并且还被处理以获得具有抑制的SNR的信号抑制图像数据。 然后，通过从信号增强图像数据中减去信号抑制图像数据来形成差分图像数据，以去除信号抑制和信号增强图像数据中基本相同的低频结构解剖背景。 一旦删除结构化背景，就执行特征提取。 对于肺结节的检测，执行像素阈值处理，随后进行阈值处理的连续像素的圆度和/或尺寸测试。 阈值水平变化，并且使用改变阈值对圆度和大小的影响来检测结节。 为了检测乳房X线摄影微钙化，进行像素阈值和连续像素区域阈值处理。 然后检测到疑似异常的群集。

公开(公告)号：US20090169087A1

公开(公告)日：2009-07-02

申请号：US12280697

申请日：2006-09-19

申请人： Kunio Doi ,  Satoshi Kasai

发明人： Kunio Doi ,  Satoshi Kasai

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。

公开(公告)号：US20050171409A1

公开(公告)日：2005-08-04

申请号：US10767342

申请日：2004-01-30

申请人： Hidetaka Arimura ,  Feng Li ,  Junji Shiraishi ,  Kunio Doi

发明人： Hidetaka Arimura ,  Feng Li ,  Junji Shiraishi ,  Kunio Doi

IPC分类号： G06T7/00 ,  G21K1/12 ,  H05G1/60

CPC分类号： G06T7/0012 ,  G06T2207/30061

摘要： A method, system, and computer program product for detecting at least one nodule in a medical image of a subject, including identifying, in the medical image, an anatomical region corresponding to at least a portion of an organ of interest; filtering the medical image to obtain a difference image; detecting, in the difference image, a first plurality of nodule candidates within the anatomical region; calculating respective nodule feature values of the first plurality of nodule candidates based on pixel values of at least one of the medical image and the difference image; removing false positive nodule candidates from the first plurality of nodule candidates based on the respective nodule feature values to obtain a second plurality of nodule candidates; and determining the at least one nodule by classifying each of the second plurality of nodule candidates as a nodule or a non-nodule based on at least one of the pixel values and the respective nodule feature values. True-positive nodules are identified using linear discriminant analysis and/or a Multi-MTANN.

摘要翻译： 一种用于检测受试者的医学图像中的至少一个结节的方法，系统和计算机程序产品，包括在医学图像中识别对应于感兴趣器官的至少一部分的解剖区域; 过滤医学图像以获得差异图像; 在所述差分图像中检测所述解剖区域内的第一多个结节候选物; 基于所述医学图像和所述差分图像中的至少一个的像素值来计算所述第一多个结节候选的各个结节特征值; 基于各个结节特征值从所述第一多个结节候选中去除假阳性结节候选，以获得第二多个结节候选; 以及基于像素值和相应结节特征值中的至少一个，将所述第二多个结节候选中的每一个分类为结节或非结节来确定所述至少一个结节。 使用线性判别分析和/或多MTANN识别真阳性结节。

公开(公告)号：US06819790B2

公开(公告)日：2004-11-16

申请号：US10120420

申请日：2002-04-12

申请人： Kenji Suzuki ,  Kunio Doi

发明人： Kenji Suzuki ,  Kunio Doi

IPC分类号： G06K962

CPC分类号： G06T7/0012

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.

摘要翻译： 训练人造神经网络（ANN）的方法包括：接收似然分布图作为教师图像，接收训练图像，在训练图像的子区域上移动局部窗口，以获得各个子区域像素组，输入 子区域像素集合到ANN，使得它提供与相应的教师图像像素值的输出像素值进行比较的输出像素值，以确定错误，并训练ANN以减少误差。 检测图像中的目标结构的方法涉及通过移动每个子区域的局部窗口扫描图像的子区域中的局部窗口，以便获得各个子区域像素组，输入子区域像素组 到ANN，使得其提供表示各个图像像素是目标结构的一部分的可能性的各个输出像素值，输出像素值共同构成似然分布图。 用于检测目标结构的另一种方法涉及在（A）相同目标结构和N个相互不同的非目标结构上训练N个并行ANN，或者（B）相同的非目标结构和N个相互不同的目标结构，ANN输出 N分别表示图像是否包括目标结构或非目标结构，并且组合N个指示以形成图像是否包括目标结构或非目标结构的组合指示。 本发明提供了存储执行方法的可执行指令的相关装置和计算机程序产品。

公开(公告)号：US06738499B1

公开(公告)日：2004-05-18

申请号：US09830574

申请日：2001-07-02

申请人： Kunio Doi ,  Katsumi Nakamura

发明人： Kunio Doi ,  Katsumi Nakamura

IPC分类号： G06K900

CPC分类号： G06T7/0012 ,  G06T2207/30061

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： 一种用于使用人造神经网络（ANN）对肺结节恶性肿瘤的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）（S4）。 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; 产生对应于结节轮廓的物理特征的客观测量; 将生成的客观措施应用于ANN; 以及基于所述ANN的输出确定所述结节恶性的可能性。 技术包括人造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的恶性可能性（S7）的估计。

公开(公告)号：US06694046B2

公开(公告)日：2004-02-17

申请号：US09818831

申请日：2001-03-28

申请人： Kunio Doi ,  Masahito Aoyama

发明人： Kunio Doi ,  Masahito Aoyama

IPC分类号： G06K900

CPC分类号： G06T7/0012

摘要： An automated method for analyzing a nodule and a computer storage medium storing computer instructions by which the method can be implemented when the instructions are loaded into a computer to program the computer. The method includes obtaining a digital image including the nodule; segmenting the nodule to obtain an outline of the nodule, including generating a difference image from chest image, identifying image intensity contour lines representative of respective image intensities in a region of interest including the nodule, and obtaining an outline of the nodule based on the image intensity contours; extracting features of the nodule based on the outline; applying features including the extracted features to at least one image classifier; and determining a likelihood of malignancy of the nodule based on the output of the at least one classifier. In one embodiment, extracted features are applied to a linear discriminant analyzer and/or an artificial neural network analyzer, the outputs of which are thresholded and the nodule determined to be non-malignant if each classifier output is below the threshold. In another embodiment, a common nodule appearing in an x-ray chest image and a CT image is segmented in each image, features extracted based on the outlines of each segmented nodule in the respective x-ray chest and CT images, and the extracted features from the x-ray chest image and CT images merged as inputs to a common classifier, with the output of the common classifier indicating the likelihood of malignancy.

摘要翻译： 一种用于分析结节的自动化方法和存储计算机指令的计算机存储介质，当指令被加载到计算机中以对计算机进行编程时，可以通过该方法实现该方法。 该方法包括获得包括结节的数字图像; 分割结节以获得结节的轮廓，包括从胸部图像生成差异图像，识别表示包括结节的感兴趣区域中的各个图像强度的图像强度轮廓线，以及基于图像获得结节的轮廓 强度轮廓; 根据轮廓提取结节的特征; 将包括提取的特征的特征应用于至少一个图像分类器; 以及基于所述至少一个分类器的输出确定所述结节恶性的可能性。 在一个实施例中，提取的特征被应用于线性判别分析器和/或人造神经网络分析器，其输出被阈值化，并且如果每个分类器输出低于阈值，则确定为非恶性的结节。 在另一个实施例中，在每个图像中分割出现在x射线胸部图像和CT图像中的共同结节，基于各个X射线胸部和CT图像中的每个分段结节的轮廓提取的特征，以及提取的特征 从X射线胸部图像和CT图像合并为公共分类器的输入，公共分类器的输出指示恶性肿瘤的可能性。

公开(公告)号：US06363163B1

公开(公告)日：2002-03-26

申请号：US09027468

申请日：1998-02-23

申请人： Xin-Wei Xu ,  Kunio Doi

发明人： Xin-Wei Xu ,  Kunio Doi

IPC分类号： G06K900

CPC分类号： G06T5/50 ,  A61B6/027 ,  G06T7/254

摘要： Method and system for the detection of interval change in medical images. Three dimensional images, such as previous and current section images in CT scans, are obtained. An anatomic feature, such as the lungs, is used to select sections containing lung by a gray-level thresholding technique. The section correspondence between the current and previous scans is determined automatically. The initial registration of the corresponding sections in the two scans is achieved by a rotation correction and a cross-correlation technique. A more accurate registration between the corresponding current and previous section images is achieved by local matching. A nonlinear warping process which is also based on the cross-correlation technique is applied to the previous image to yield a warped image after the matching. The final subtracted section images were derived by subtracting of the previous section images from the corresponding current section images. Interval changes such as a change in tumor size and a newly developed pleural effusion are enhanced significantly.

摘要翻译： 用于检测医学图像间隔变化的方法和系统。 获得三维图像，例如CT扫描中的先前和当前部分图像。 解剖特征，如肺，用于通过灰度阈值技术选择含有肺的部位。 自动确定当前扫描和以前扫描之间的部分对应关系。 通过旋转校正和互相关技术来实现两次扫描中相应部分的初始配准。 通过局部匹配实现相应的当前和前一个部分图像之间更准确的配准。 还将基于互相关技术的非线性翘曲过程应用于先前的图像，以在匹配之后产生翘曲图像。 通过从相应的当前部分图像中减去前一部分图像来导出最终减法部分图像。 肿瘤大小变化和新发胸腔积液等间期变化明显增强。