公开(公告)号：US06201137B1

公开(公告)日：2001-03-13

申请号：US09319031

申请日：1999-08-11

申请人： Hiroshi Nakaoka ,  Toshiro Kubota

发明人： Hiroshi Nakaoka ,  Toshiro Kubota

IPC分类号： C07D30706

CPC分类号： C07D307/08

摘要： Tetrahydrofurans suitable for use as starting materials in the synthesis of polyether polyols without causing coloration in the product polyether polyols can be obtained by contacting crude tetrahydrofurans with a mineral acid or a strongly acidic cation-exchange resin, followed by isolation through a simple distillation and/or rectification to obtain purified tetrahydrofurans.

摘要翻译： 可以通过使粗四氢呋喃与无机酸或强酸性阳离子交换树脂接触，然后通过简单的蒸馏和/或反应产物分离来获得适合用作聚醚多元醇合成中的原料而不引起产物聚醚多元醇着色的四氢呋喃， 或精馏以获得纯化的四氢呋喃。

公开(公告)号：US07756313B2

公开(公告)日：2010-07-13

申请号：US11592869

申请日：2006-11-03

申请人： Jinbo Bi ,  Senthil Periaswamy ,  Kazunori Okada ,  Toshiro Kubota ,  Glenn Fung ,  Marcos Salganicoff ,  R. Bharat Rao

发明人： Jinbo Bi ,  Senthil Periaswamy ,  Kazunori Okada ,  Toshiro Kubota ,  Glenn Fung ,  Marcos Salganicoff ,  R. Bharat Rao

IPC分类号： G06K9/00

CPC分类号： G06K9/6256 ,  G06K9/6282

摘要： A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.

摘要翻译： 一种用于计算机辅助检测医学图像中的解剖异常的方法，包括提供所述异常候选的多个异常候选和特征，并且使用形式符号的线性分类器（wTx）的分级级联将所述异常候选分类为真正的肯定或假阳性 + b），其中x是特征向量，w是加权向量，b是模型参数，其中使用不同权重来惩罚假否定和假阳性，并且其中对于级联的每个连续阶段使用更复杂的特征 的分类器。

公开(公告)号：US07720271B2

公开(公告)日：2010-05-18

申请号：US11398100

申请日：2006-04-04

申请人： Toshiro Kubota

发明人： Toshiro Kubota

IPC分类号： G06K9/00

CPC分类号： G06K9/34 ,  G06K9/38 ,  G06T7/12 ,  G06T7/149 ,  G06T7/62 ,  G06T2207/10081 ,  G06T2207/20116 ,  G06T2207/30064

摘要： A reactive-diffusion method for estimating a diameter of an object of interest includes providing a volume of interest including a plurality of voxels, initializing at least two volumes of the volume of interest, wherein each of the voxels has at least two values corresponding to the at least two volumes of the volume of interest, respectively, performing a diffusion operation and a reaction operation on the voxels to adjust the at least two values, comparing, for each voxel, the at least two values to a threshold to assign each voxel to one of the at least two volumes, wherein the assignment of the voxels is a segmentation result, and estimating a diameter of the object of interest from the segmentation result, wherein the object of interest is represented by at least one of the at least two volumes but less than all the volumes.

摘要翻译： 用于估计感兴趣对象的直径的反应性扩散方法包括提供包括多个体素的兴趣体积，初始化感兴趣体积的至少两个体积，其中每个体素具有对应于 至少两个体积的感兴趣的体积分别对体素执行扩散操作和反应操作以调整至少两个值，将每个体素比较所述至少两个值到阈值以将每个体素分配到 所述至少两个体积中的一个，其中所述体素的分配是分割结果，以及从所述分割结果估计所述感兴趣对象的直径，其中所述感兴趣对象由所述至少两个体积中的至少一个表示 但少于所有卷。

公开(公告)号：US20090092302A1

公开(公告)日：2009-04-09

申请号：US12243387

申请日：2008-10-01

申请人： Toshiro Kubota ,  Anna Jerebko ,  Marcos Salganicoff

发明人： Toshiro Kubota ,  Anna Jerebko ,  Marcos Salganicoff

IPC分类号： G06K9/00

CPC分类号： G06T7/0012 ,  G06T7/11 ,  G06T7/187 ,  G06T2207/30064

摘要： A method for differentiating pulmonary nodules in digitized medical images includes identifying an object of interest from a digital image of the lungs, computing a first distance map of each point of the object of interest, determining a seed point from the first distance map, starting from the seed point, growing a first region by adding successive adjacent layers of points until a background point is reached, and partitioning the first region into a nodule region and a non-nodule region.

摘要翻译： 用于区分数字化医学图像中的肺结节的方法包括从肺的数字图像识别感兴趣对象，计算感兴趣对象的每个点的第一距离图，从第一距离图确定种子点，从 种子点，通过添加连续的相邻层点直到达到背景点来生长第一区域，并将第一区域划分成结节区域和非结节区域。

公开(公告)号：US07876938B2

公开(公告)日：2011-01-25

申请号：US11542477

申请日：2006-10-03

申请人： Xiaolei Huang ,  Xiang Zhou ,  Anna Jerebko ,  Arun Krishnan ,  Haiying Guan ,  Toshiro Kubota ,  Vaclav Potesil

发明人： Xiaolei Huang ,  Xiang Zhou ,  Anna Jerebko ,  Arun Krishnan ,  Haiying Guan ,  Toshiro Kubota ,  Vaclav Potesil

IPC分类号： G06K9/00 ,  G06K9/62

CPC分类号： G06T7/174 ,  G06T7/155 ,  G06T7/33 ,  G06T7/38 ,  G06T2207/10072 ,  G06T2207/20016 ,  G06T2207/20081 ,  G06T2207/20164 ,  G06T2207/30004 ,  G06T2207/30204

摘要： A method for segmenting digitized images includes providing a training set comprising a plurality of digitized whole-body images, providing labels on anatomical landmarks in each image of said training set, aligning each said training set image, generating positive and negative training examples for each landmark by cropping the aligned training volumes into one or more cropping windows of different spatial scales, and using said positive and negative examples to train a detector for each landmark at one or more spatial scales ranging from a coarse resolution to a fine resolution, wherein the spatial relationship between a cropping windows of a coarse resolution detector and a fine resolution detector is recorded.

摘要翻译： 用于分割数字化图像的方法包括提供包括多个数字化的全身图像的训练集合，在所述训练集合的每个图像中的解剖学标志上提供标签，对准每个所述训练集合图像，为每个界标生成正和负训练样本 通过将对准的训练量裁剪成不同空间尺度的一个或多个裁剪窗口，并且使用所述正和负的示例来在从粗分辨率到精细分辨率的一个或多个空间尺度上训练每个地标的检测器，其中空间 记录粗分辨率检测器的裁切窗与精细分辨率检测器之间的关系。

公开(公告)号：US20070110292A1

公开(公告)日：2007-05-17

申请号：US11592869

申请日：2006-11-03

申请人： Jinbo Bi ,  Senthil Periaswamy ,  Kazunori Okada ,  Toshiro Kubota ,  Glenn Fung ,  Marcos Salganicoff ,  R. Rao

发明人： Jinbo Bi ,  Senthil Periaswamy ,  Kazunori Okada ,  Toshiro Kubota ,  Glenn Fung ,  Marcos Salganicoff ,  R. Rao

IPC分类号： G06K9/00

CPC分类号： G06K9/6256 ,  G06K9/6282

摘要： A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.

摘要翻译： 一种用于计算机辅助检测医学图像中的解剖异常的方法，包括提供所述异常候选的多个异常候选和特征，并且使用形式符号（w）的线性分类器的分级级联将所述异常候选分类为真阳性或假阳性 x + b），其中x是特征向量，w是加权向量，b是模型参数，其中不同的权重用于惩罚假否定和假肯定，并且其中更复杂的特征 用于分级器级联的每个连续阶段。

公开(公告)号：US20060228014A1

公开(公告)日：2006-10-12

申请号：US11398100

申请日：2006-04-04

申请人： Toshiro Kubota

发明人： Toshiro Kubota

IPC分类号： G06K9/00

CPC分类号： G06K9/34 ,  G06K9/38 ,  G06T7/12 ,  G06T7/149 ,  G06T7/62 ,  G06T2207/10081 ,  G06T2207/20116 ,  G06T2207/30064

摘要： A reactive-diffusion method for estimating a diameter of an object of interest includes providing a volume of interest including a plurality of voxels, initializing at least two volumes of the volume of interest, wherein each of the voxels has at least two values corresponding to the at least two volumes of the volume of interest, respectively, performing a diffusion operation and a reaction operation on the voxels to adjust the at least two values, comparing, for each voxel, the at least two values to a threshold to assign each voxel to one of the at least two volumes, wherein the assignment of the voxels is a segmentation result, and estimating a diameter of the object of interest from the segmentation result, wherein the object of interest is represented by at least one of the at least two volumes but less than all the volumes.

摘要翻译： 用于估计感兴趣对象的直径的反应性扩散方法包括提供包括多个体素的兴趣体积，初始化感兴趣体积的至少两个体积，其中每个体素具有至少两个对应于 至少两个体积的感兴趣的体积分别对体素执行扩散操作和反应操作以调整至少两个值，将每个体素比较所述至少两个值到阈值以将每个体素分配到 所述至少两个体积中的一个，其中所述体素的分配是分割结果，以及从所述分割结果估计所述感兴趣对象的直径，其中所述感兴趣对象由所述至少两个体积中的至少一个表示 但少于所有卷。

公开(公告)号：US20060228013A1

公开(公告)日：2006-10-12

申请号：US11398099

申请日：2006-04-04

申请人： Toshiro Kubota ,  Kazunori Okada

发明人： Toshiro Kubota ,  Kazunori Okada

IPC分类号： G06K9/00

CPC分类号： G06K9/44 ,  G06K2209/053 ,  G06T7/0012 ,  G06T7/12 ,  G06T7/149 ,  G06T7/62 ,  G06T2207/10081 ,  G06T2207/20116 ,  G06T2207/30064 ,  Y10S378/901

摘要： A method for estimating a diameter of an object of interest, includes extracting a sub-volume of voxels around a marker identifying a location of the object of interest, and determining a segmentation result identifying an edge of the object of interest by a reactive-diffusion method. The method further includes performing a boundary check on the segmentation result to determine whether the object of interest is solitary, estimating a diameter of the object of interest from the segmentation result upon determining the object of interest to be solitary, and extracting the object of interest by performing ellipsoid fit on the sub-volume and estimating a diameter of the object of interest upon determining the object of interest to be non-solitary.

公开(公告)号：US08165369B2

公开(公告)日：2012-04-24

申请号：US12243387

申请日：2008-10-01

申请人： Toshiro Kubota ,  Anna Jerebko ,  Marcos Salganicoff

发明人： Toshiro Kubota ,  Anna Jerebko ,  Marcos Salganicoff

IPC分类号： G06K9/00

CPC分类号： G06T7/0012 ,  G06T7/11 ,  G06T7/187 ,  G06T2207/30064

摘要： A method for differentiating pulmonary nodules in digitized medical images includes identifying an object of interest from a digital image of the lungs, computing a first distance map of each point of the object of interest, determining a seed point from the first distance map, starting from the seed point, growing a first region by adding successive adjacent layers of points until a background point is reached, and partitioning the first region into a nodule region and a non-nodule region.

摘要翻译： 用于区分数字化医学图像中的肺结节的方法包括从肺的数字图像识别感兴趣对象，计算感兴趣对象的每个点的第一距离图，从第一距离图确定种子点，从 种子点，通过添加连续的相邻层点直到达到背景点来生长第一区域，并将第一区域划分成结节区域和非结节区域。

公开(公告)号：US07711172B2

公开(公告)日：2010-05-04

申请号：US11970899

申请日：2008-01-08

申请人： Toshiro Kubota

发明人： Toshiro Kubota

IPC分类号： G06K9/00

CPC分类号： G06T7/0012 ,  G06K9/342 ,  G06K2209/05 ,  G06T7/11 ,  G06T7/194 ,  G06T7/62 ,  G06T2207/10081 ,  G06T2207/20101 ,  G06T2207/30064

摘要： A method for automatically estimating a measure of a nodule includes providing sliced image volume data comprising at least one marked position of the nodule, extracting a sub-volume from the sliced image volume data that includes the marked position, performing a figure-ground separation to determine a solid foreground of the sub-volume and a non-solid foreground of the sub-volume, determining a seed point corresponding to the nodule from each of the solid foreground and the non-solid foreground, selecting one of the seed point of the solid foreground or the seed point of the non-solid foreground, determining whether the selected seed point is solitary or non-solitary and performing region growing for non-solitary selected seed point, determining a segmentation of the selected seed point, estimating the measure of the nodule corresponding to the segmentation, and outputting the segmentation and an estimate of the measure of the nodule.

摘要翻译： 用于自动估计结节的测量的方法包括提供包括结节的至少一个标记位置的切片图像体积数据，从包括标记位置的切片图像体积数据中提取子体积，执行图形分离 确定子体积的固体前景和子体积的非固体前景，从固体前景和非固体前景中的每一个确定与结节相对应的种子点，选择一个种子点 确定非固体前景的固定前景或种子点，确定选择的种子点是单独的还是非孤立的，并且对于非孤立选择的种子点执行区域生长，确定所选种子点的分割，估计所选种子点的度量 所述结节对应于所述分割，并且输出所述分割和所述结节的度量的估计。