专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明授权

US09710916B2 Kernel sparse models for automated tumor segmentation 有权
公开(公告)号：US09710916B2
公开(公告)日：2017-07-18
申请号：US14853617
申请日：2015-09-14
申请人： Jayaraman Jayaraman Thiagarajan , Karthikeyan Ramamurthy , Andreas Spanias , David Frakes
发明人： Jayaraman Jayaraman Thiagarajan , Karthikeyan Ramamurthy , Andreas Spanias , David Frakes
IPC分类号： G06K9/00 , G06T7/00 , A61B5/055 , G06T7/11
CPC分类号： G06T7/0012 , A61B5/055 , A61B2576/026 , G06T7/11 , G06T2207/10088 , G06T2207/20081 , G06T2207/30096
摘要： A robust method to automatically segment and identify tumor regions in medical images is extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an efficient algorithm uses sparse models in feature spaces to identify pixels belonging to tumorous regions. By fusing both intensity and spatial location information of the pixels, this technique can automatically localize tumor regions without user intervention. Using a few expert-segmented training images, a sparse coding-based classifier is learned. For a new test image, the sparse code obtained from every pixel is tested with the classifier to determine if it belongs to a tumor region. Particular embodiments also provide a highly accurate, low-complexity procedure for cases when the user can provide an initial estimate of the tumor in a test image.

2. 发明申请

US20160005183A1 KERNEL SPARSE MODELS FOR AUTOMATED TUMOR SEGMENTATION 有权
标题翻译：用于自动肿瘤分期的KERNEL SPARSE模型
公开(公告)号：US20160005183A1
公开(公告)日：2016-01-07
申请号：US14853617
申请日：2015-09-14
申请人： Jayaraman Jayaraman Thiagarajan , Karthikeyan Ramamurthy , Andreas Spanias , David Frakes
发明人： Jayaraman Jayaraman Thiagarajan , Karthikeyan Ramamurthy , Andreas Spanias , David Frakes
IPC分类号： G06T7/00 , A61B5/055
CPC分类号： G06T7/0012 , A61B5/055 , A61B2576/026 , G06T7/11 , G06T2207/10088 , G06T2207/20081 , G06T2207/30096
摘要： A robust method to automatically segment and identify tumor regions in medical images is extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an efficient algorithm uses sparse models in feature spaces to identify pixels belonging to tumorous regions. By fusing both intensity and spatial location information of the pixels, this technique can automatically localize tumor regions without user intervention. Using a few expert-segmented training images, a sparse coding-based classifier is learned. For a new test image, the sparse code obtained from every pixel is tested with the classifier to determine if it belongs to a tumor region. Particular embodiments also provide a highly accurate, low-complexity procedure for cases when the user can provide an initial estimate of the tumor in a test image.
摘要翻译：在医学图像中自动分割和识别肿瘤区域的有力方法对于临床诊断和疾病建模非常有价值。在各种实施例中，有效算法使用特征空间中的稀疏模型来识别属于肿瘤区域的像素。通过融合像素的强度和空间位置信息，该技术可以自动定位肿瘤区域而无需用户干预。使用几个专家分割的训练图像，学习了一种基于稀疏编码的分类器。对于新的测试图像，用分类器测试从每个像素获得的稀疏码，以确定它是否属于肿瘤区域。当用户可以在测试图像中提供肿瘤的初始估计时，特定实施例还提供了高精度，低复杂度的程序。

3. 发明申请

US20160005170A1 MEASURING GLOMERULAR NUMBER FROM KIDNEY MRI IMAGES 有权
标题翻译：从KIDNEY MRI图像中测量GLOMERULAR数
公开(公告)号：US20160005170A1
公开(公告)日：2016-01-07
申请号：US14853645
申请日：2015-09-14
申请人： Jayaraman Jayaraman Thiagarajan , Karthikeyan Ramamurthy , Andreas Spanias , David Frakes
发明人： Jayaraman Jayaraman Thiagarajan , Karthikeyan Ramamurthy , Andreas Spanias , David Frakes
IPC分类号： G06T7/00 , G06T11/60 , G06K9/62
CPC分类号： G06T7/0012 , A61B5/055 , A61B5/1075 , A61B5/20 , G06K9/6269 , G06T11/60 , G06T2207/10004 , G06T2207/10088
摘要： Measuring the number of glomeruli in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. In particular, a recent Magnetic Resonance Imaging (MRI) technique, based on injection of a contrast agent, cationic ferritin, has been effective in identifying glomerular regions in the kidney. In various embodiments, a low-complexity, high accuracy method for obtaining the glomerular count from such kidney MRI images is described. This method employs a patch-based approach for identifying a low-dimensional embedding that enables the separation of glomeruli regions from the rest. By using only a few images marked by the expert for learning the model, the method provides an accurate estimate of the glomerular number for any kidney image obtained with the contrast agent. In addition, the implementation of our method shows that this method is near real-time, and can process about 5 images per second.
摘要翻译：使用非破坏性技术测量整个完整肾中的肾小球数量对于研究几种肾脏和全身疾病是非常重要的。特别地，基于注射造影剂阳离子铁蛋白的最近的磁共振成像（MRI）技术已经有效地鉴定了肾脏中的肾小球区域。在各种实施例中，描述了用于从这样的肾MRI图像获得肾小球计数的低复杂度，高精度方法。该方法采用基于贴片的方法来识别能够将肾小球区域与其余部分分离的低维度嵌入。通过仅使用由专家标记的几个图像来学习模型，该方法提供了用造影剂获得的任何肾脏图像的肾小球数的准确估计。此外，我们的方法的实现表明这种方法接近实时，每秒可以处理大约5张图像。

4. 发明授权

US09779497B2 Measuring glomerular number from kidney MRI images 有权
公开(公告)号：US09779497B2
公开(公告)日：2017-10-03
申请号：US14853645
申请日：2015-09-14
申请人： Jayaraman Jayaraman Thiagarajan , Karthikeyan Ramamurthy , Andreas Spanias , David Frakes
发明人： Jayaraman Jayaraman Thiagarajan , Karthikeyan Ramamurthy , Andreas Spanias , David Frakes
IPC分类号： G06K9/00 , G06T7/00 , A61B5/055 , A61B5/20 , A61B5/107 , G06K9/62 , G06T11/60 , A61P13/12
CPC分类号： G06T7/0012 , A61B5/055 , A61B5/1075 , A61B5/20 , G06K9/6269 , G06T11/60 , G06T2207/10004 , G06T2207/10088
摘要： Measuring the number of glomeruli in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. In particular, a recent Magnetic Resonance Imaging (MRI) technique, based on injection of a contrast agent, cationic ferritin, has been effective in identifying glomerular regions in the kidney. In various embodiments, a low-complexity, high accuracy method for obtaining the glomerular count from such kidney MRI images is described. This method employs a patch-based approach for identifying a low-dimensional embedding that enables the separation of glomeruli regions from the rest. By using only a few images marked by the expert for learning the model, the method provides an accurate estimate of the glomerular number for any kidney image obtained with the contrast agent. In addition, the implementation of our method shows that this method is near real-time, and can process about 5 images per second.

5. 发明申请

US20160012314A1 ENSEMBLE SPARSE MODELS FOR IMAGE ANALYSIS AND RESTORATION 有权
标题翻译：用于图像分析和恢复的可靠的SPARSE模型
公开(公告)号：US20160012314A1
公开(公告)日：2016-01-14
申请号：US14772343
申请日：2014-03-14
申请人： Karthikeyan RAMAMURTHY , Andreas SPANIAS , Prasanna SATTIGERI , Jayaraman THIAGARAJAN , ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIIVERSITY
发明人： Karthikeyan RAMAMURTHY , Jayaraman THIAGARAJAN , Prasanna SATTIGERI , Andreas SPANIAS
IPC分类号： G06K9/62
CPC分类号： G06K9/6244 , G06K9/6255 , G06K9/6256 , G06K2009/4666
摘要： Methods and systems for recovering corrupted/degraded images using approximations obtained from an ensemble of multiple sparse models are disclosed. Sparse models may represent images parsimoniously using elementary patterns from a “dictionary” matrix. Various embodiments of the present disclosure involve simple and computationally efficient dictionary design approach along with low-complexity reconstruction procedure that may use a parallel-friendly table-lookup process. Multiple dictionaries in an ensemble model may be inferred sequentially using greedy forward-selection approach and can incorporate bagging/boosting strategies, taking into account application-specific degradation. Recovery performance obtained using the proposed approaches with image super resolution and compressive recovery can be comparable to or better than existing sparse modeling based approaches, at reduced computational complexity. By including ensemble models in hierarchical multilevel learning, where multiple dictionaries are inferred in each level, further performance improvements can be obtained in image recovery, without significant increase in computational complexity.
摘要翻译：公开了使用从多个稀疏模型的集合获得的近似来恢复损坏/退化的图像的方法和系统。稀疏模型可以使用“字典”矩阵中的基本模式简单地表示图像。本公开的各种实施例涉及简单和计算上有效的字典设计方法以及可以使用并行友好的表查找过程的低复杂度重建过程。综合模型中的多个词典可以使用贪心的前向选择方法顺序推断，并且可以考虑到应用程序特定的降级，并入包装/提升策略。使用所提出的具有图像超分辨率和压缩恢复的方法获得的恢复性能可以在降低的计算复杂度的情况下与现有的基于稀疏建模的方法相比或更好。通过在层级多层次学习中包含集体模型，在每个级别推断出多个字典，在图像恢复中可以获得进一步的性能改进，而不会显着增加计算复杂度。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式