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1. 发明授权

US07177471B2 Shape priors for level set representations 有权
标题翻译：形成级别表示的先验
公开(公告)号：US07177471B2
公开(公告)日：2007-02-13
申请号：US10356093
申请日：2003-01-31
申请人： Nikolaos Paraglos , Mikael Rousson
发明人： Nikolaos Paraglos , Mikael Rousson
IPC分类号： G06K9/46
CPC分类号： G06K9/00362 , G06K9/48 , G06K9/6206 , G06T7/12 , G06T7/149
摘要： This invention relates to shape priors for level set representations. An embodiment of the invention comprises a first stage and a second stage. In the first stage, a shape model can be built directly on level set space using a collection of samples. The shape model can be constructed using a variational framework to create a non-stationary pixel-wise model that accounts for shape variabilities. Then, in the second stage, the shape model can be used as basis to introduce the shape prior in an energetic form. In terms of level set representations, the shape prior aims at minimizing non-stationary distance between the evolving interface and the shape model. An embodiment according to the present invention can be integrated with an existing, data-driven variational method to perform image segmentation for physically corrupted and incomplete data.
摘要翻译：本发明涉及水平集表示的形状先验。本发明的实施例包括第一阶段和第二阶段。在第一阶段，可以使用样本集合直接在级别集空间上构建形状模型。可以使用变分框架来构建形状模型，以创建考虑到形状变异性的非平稳像素模型。然后，在第二阶段中，形状模型可以用作以能量形式引入形状的基础。在水平集表示方面，先前的形状旨在最小化不断变化的界面和形状模型之间的非固定距离。根据本发明的实施例可以与现有的数据驱动变分方法集成，以对物理损坏和不完整的数据执行图像分割。

2. 发明授权

US08131038B2 System and method for global-to-local shape matching for automatic liver segmentation in medical imaging 有权
标题翻译：用于医学成像中自动肝分割的全局到局部形状匹配的系统和方法
公开(公告)号：US08131038B2
公开(公告)日：2012-03-06
申请号：US12189276
申请日：2008-08-11
申请人： Kinda Anna Saddi , Mikael Rousson
发明人： Kinda Anna Saddi , Mikael Rousson
IPC分类号： G06K9/00
CPC分类号： G06K9/6209 , G06K2209/051 , G06T7/11 , G06T2207/20161 , G06T2207/30056
摘要： A method for automatically segmenting a liver in digital medical images includes providing a 3-dimensional (3D) digital image I and a set of N training shapes {φi}i=1, . . . , N for a liver trained from a set of manually segmented images, selecting a seed point to initialize the segmentation, representing a level set function φα(θx+h) of a liver boundary Γ in the image as ϕ α ⁡ ( x ) = ϕ 0 + ∑ i = 1 n ⁢ α i ⁢ V i ⁡ ( x ) , ⁢ where ⁢ ϕ 0 ⁡ ( x ) = 1 N ⁢ ∑ i = 1 N ⁢ ϕ i ⁡ ( x ) is a mean shape, {Vi(x)}i=1, . . . , n are eigenmodes where n
摘要翻译：用于在数字医学图像中自动分割肝脏的方法包括提供三维（3D）数字图像I和一组N个训练形状{＆phgr; i} i = 1。。。，用于从一组手动分割图像训练的肝脏，选择种子点来初始化分割，代表肝脏边界＆Ggr的水平集函数＆lt;α（＆amp; t; x + h）; 在形象与＆phis; α⁡（x）=＆phis; 0 +Σi = 1nαi V i⁡（x），where＆phis; 0⁡（x）= 1NΣi= 1 N＆phis; i⁡（x）是平均形状，{Vi（x）} i = 1，。。。，n是本征模式，其中n

3. 发明授权

US07773806B2 Efficient kernel density estimation of shape and intensity priors for level set segmentation 失效
标题翻译：用于水平集分割的形状和强度优先级的有效核密度估计
公开(公告)号：US07773806B2
公开(公告)日：2010-08-10
申请号：US11397040
申请日：2006-04-03
申请人： Daniel Cremers , Mikael Rousson
发明人： Daniel Cremers , Mikael Rousson
IPC分类号： G06K9/34 , G06K9/62
CPC分类号： G06K9/6207 , G06T7/12 , G06T7/143 , G06T7/149 , G06T2207/10132 , G06T2207/30004
摘要： Methods and systems for image segmentation are disclosed. A nonlinear statistical shape model of an image is integrated with a non-parametric intensity model to estimate characteristics of an image and create segmentations of an image based on Bayesian inference from characteristics of prior learned images based on the same models.
摘要翻译：公开了用于图像分割的方法和系统。图像的非线性统计形状模型与非参数强度模型集成以估计图像的特征，并且基于基于相同模型的先前学习图像的特征，基于贝叶斯推理创建图像的分割。

4. 发明申请

US20070127814A1 Method and system for histogram calculation using a graphics processing unit 有权
标题翻译：使用图形处理单元进行直方图计算的方法和系统
公开(公告)号：US20070127814A1
公开(公告)日：2007-06-07
申请号：US11594460
申请日：2006-11-08
申请人： Oliver Fluck , Shmuel Aharon , Mikael Rousson , Daniel Cremers
发明人： Oliver Fluck , Shmuel Aharon , Mikael Rousson , Daniel Cremers
IPC分类号： G06K9/34
CPC分类号： G06T1/20
摘要： A method for histogram calculation using a graphics processing unit (GPU), comprises storing image data in a two-dimensional (2D) texture domain; subdividing the domain into independent regions or tiles; calculating in parallel, in a GPU, a plurality of tile histograms, one for each tile; and summing up in parallel, in the GPU, the tile histograms so as to derive a final image histogram.
摘要翻译：一种使用图形处理单元（GPU）进行直方图计算的方法，包括将图像数据存储在二维（2D）纹理域中; 将域细分为独立区域或瓦片; 在GPU中并行计算多个瓦片直方图，每个瓦片一个; 并且在GPU中并行地并行地绘制瓦片直方图，以便导出最终图像直方图。

5. 发明授权

US07889941B2 Efficient segmentation of piecewise smooth images 有权
标题翻译：分段平滑图像的高效分割
公开(公告)号：US07889941B2
公开(公告)日：2011-02-15
申请号：US11696869
申请日：2007-04-05
申请人： Jerome Piovano , Mikael Rousson
发明人： Jerome Piovano , Mikael Rousson
IPC分类号： G06K9/44
CPC分类号： G06T7/149 , G06T7/11 , G06T2207/20161
摘要： A fast and robust segmentation model for piecewise smooth images is provided. Local statistics in an energy formulation are provided as a functional. The shape gradient of this new functional gives a contour evolution controlled by local averaging of image intensities inside and outside the contour. Fast computation is realized by expressing terms as the result of convolutions implemented via recursive filters. Results are similar to the general Mumford-Shah model but realized faster without having to solve a Poisson partial differential equation at each iteration. Examples are provided. A system to implement segmentation methods is also provided.
摘要翻译：提供了一种用于分段平滑图像的快速且鲁棒的分割模型。提供能量公式中的本地统计作为功能。这种新功能的形状梯度给出了通过轮廓内部和外部的图像强度的局部平均来控制的轮廓演化。通过表达术语作为通过递归滤波器实现的卷积的结果来实现快速计算。结果与一般的Mumford-Shah模型相似，但实现得更快，而无需在每次迭代中解决泊松偏微分方程。提供了实例。还提供了一种实现分割方法的系统。

6. 发明申请

US20090052756A1 SYSTEM AND METHOD FOR GLOBAL-TO-LOCAL SHAPE MATCHING FOR AUTOMATIC LIVER SEGMENTATION IN MEDICAL IMAGING 有权
公开(公告)号：US20090052756A1
公开(公告)日：2009-02-26
申请号：US12189276
申请日：2008-08-11
申请人： Kinda Anna Saddi , Mikael Rousson
发明人： Kinda Anna Saddi , Mikael Rousson
IPC分类号： G06K9/34
CPC分类号： G06K9/6209 , G06K2209/051 , G06T7/11 , G06T2207/20161 , G06T2207/30056
摘要： A method for automatically segmenting a liver in digital medical images includes providing a 3-dimensional (3D) digital image I and a set of N training shapes {φi}i=1, . . . , N for a liver trained from a set of manually segmented images, selecting a seed point to initialize the segmentation, representing a level set function φα(θx+h) of a liver boundary Γ in the image as φ α  ( x ) = φ 0 + ∑ i = 1 n  α i  V i  ( x ) ,  where  φ 0  ( x ) = 1 N  ∑ i = 1 N  φ i  ( x ) is a mean shape, {Vi(x)}i=1, . . . , n are eigenmodes where n

7. 发明申请

US20070014462A1 Constrained surface evolutions for prostate and bladder segmentation in CT images 审中-公开
标题翻译： CT图像中前列腺和膀胱分割的约束表面演变
公开(公告)号：US20070014462A1
公开(公告)日：2007-01-18
申请号：US11452169
申请日：2006-06-13
申请人： Mikael Rousson , Ali Khamene , Mamadou Diallo
发明人： Mikael Rousson , Ali Khamene , Mamadou Diallo
IPC分类号： G06K9/00 , G06K9/34
CPC分类号： G06T7/149 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/20101 , G06T2207/20161 , G06T2207/30004 , G06T2207/30081
摘要： A Bayesian formulation for coupled surface evolutions in level set methods and application to the segmentation of the prostate and the bladder in CT images are disclosed. A Bayesian framework imposing a shape constraint on the prostate is also disclosed, while coupling its shape extraction with that of the bladder. Constraining the segmentation process improves the extraction of both organs' shapes.
摘要翻译：公开了一种用于水平集方法中耦合表面演化的贝叶斯公式，用于CT图像中前列腺和膀胱分割的应用。还公开了对前列腺施加形状约束的贝叶斯框架，同时将其形状提取与膀胱的形状提取相结合。限制分割过程改善了两个器官形状的提取。

8. 发明授权

US08488873B2 Method of computing global-to-local metrics for recognition 有权
标题翻译：计算用于识别的全局到本地度量的方法
公开(公告)号：US08488873B2
公开(公告)日：2013-07-16
申请号：US12574717
申请日：2009-10-07
申请人： Mikael Rousson , Jan Erik Solem , Jerome Piovano
发明人： Mikael Rousson , Jan Erik Solem , Jerome Piovano
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/6215 , G06N99/005
摘要： A method of computing global-to-local metrics for recognition. Based on training examples with feature representations, the method automatically computes a local metric that varies over the space of feature representations to optimize discrimination and the performance of recognition systems.Given a set of points in an arbitrary features space, local metrics are learned in a hierarchical manner that give low distances between points of same class and high distances between points of different classes. Rather than considering a global metric, a class-based metric or a point-based metric, the proposed invention applies successive clustering to the data and associates a metric to each one of the clusters.
摘要翻译：计算用于识别的全局到本地度量的方法。基于具有特征表示的训练示例，该方法自动计算在特征表示空间上变化的局部度量，以优化识别系统的识别和性能。给定任意特征空间中的一组点，以分级方式学习局部度量，这样可以在不同类别的点之间提供相同类别的点和高距离之间的较低距离。所提出的发明不是考虑全局度量，基于类的度量或基于点的度量，而是将连续的聚类应用于数据并将度量与每个集群相关联。

9. 发明授权

US07809190B2 General framework for image segmentation using ordered spatial dependency 有权
标题翻译：使用有序空间依赖的图像分割的一般框架
公开(公告)号：US07809190B2
公开(公告)日：2010-10-05
申请号：US11684785
申请日：2007-03-12
申请人： Mikael Rousson , Chenyang Xu
发明人： Mikael Rousson , Chenyang Xu
IPC分类号： G06K9/34
CPC分类号： G06K9/72 , G06K9/34 , G06K2209/05 , G06T7/12 , G06T7/149 , G06T2207/10072 , G06T2207/20041 , G06T2207/20128 , G06T2207/20161 , G06T2207/30016
摘要： A general framework to enhance performance of automatic segmentation of a plurality of structures in medical imaging applications incorporates inter-structure spatial dependencies in to existing segmentation algorithms. Ranking the structures according to their dependencies allows a hierarchical approach to automatically segmenting multiple structures that improves each individual segmentation and provides automatic initializations.
摘要翻译：用于增强医学成像应用中多个结构的自动分割的性能的一般框架包含现有分割算法中的结构间空间相关性。根据其依赖性排列结构允许分层方法自动分割多个结构，从而改善每个单独的分段并提供自动初始化。

10. 发明授权

US07773789B2 Probabilistic minimal path for automated esophagus segmentation 有权
标题翻译：自动食管分割的概率最小路径
公开(公告)号：US07773789B2
公开(公告)日：2010-08-10
申请号：US11481992
申请日：2006-07-05
申请人： Mikael Rousson , Chenyang Xu , Ying Bai
发明人： Mikael Rousson , Chenyang Xu , Ying Bai
IPC分类号： G06K9/00
CPC分类号： G06K9/34 , G06K9/72 , G06K2209/05
摘要： A segmentation of the esophagus from image data by specifying only the two end points is disclosed. Surrounding structures are used as high-level constraints to construct shape and appearance models. Prior shape information is integrated for the segmentation of a new esophagus using a Bayesian formulation. This permits to automatically select the proper models. Given the end points, a shortest path algorithm provides the optimal esophagus according to the Bayesian formulation.
摘要翻译：公开了通过仅指定两个端点从图像数据分割食道。围绕结构被用作构建形状和外观模型的高级约束。整合了先前的形状信息，以便使用贝叶斯公式对新食管进行分割。这样可以自动选择合适的型号。给定终点，最短路径算法根据贝叶斯公式提供最佳食道。

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