-
公开(公告)号：US20160005183A1
公开(公告)日：2016-01-07
申请号：US14853617
申请日：2015-09-14
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。
-
公开(公告)号：US09779497B2
公开(公告)日：2017-10-03
申请号：US14853645
申请日：2015-09-14
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.
-
公开(公告)号：US20160005170A1
公开(公告)日：2016-01-07
申请号：US14853645
申请日：2015-09-14
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。
-
公开(公告)号：US09710916B2
公开(公告)日：2017-07-18
申请号：US14853617
申请日：2015-09-14
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.
-
-
-
搜索结果
4 条记录 (耗时 0.01 秒)
