公开(公告)号：US10238368B2

公开(公告)日：2019-03-26

申请号：US14033452

申请日：2013-09-21

Applicant: General Electric Company

Inventor： Rakesh Mullick ,  Pavan Kumar Veerabhadra Annangi ,  Xiaoxing Li ,  Vidya Pundalik Kamath ,  Fei Zhao ,  Vivek Prabhakar Vaidya ,  Soma Biswas

IPC: A61B8/08 ,  G06T7/00 ,  G06T7/41

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring ultrasound information, determining discriminative descriptors that describe the texture of a candidate lesion region, and classifying each of the discriminative descriptors as one of a top boundary pixel, a lesion interior pixel, a lower boundary pixel, or a normal tissue pixel. The method also includes determining a pattern of transitions between the classified discriminative descriptors, and classifying the candidate lesion region as a lesion or normal tissue based on the pattern of transitions between the classified discriminative descriptors.

公开(公告)号：US09277902B2

公开(公告)日：2016-03-08

申请号：US14088068

申请日：2013-11-22

Applicant: General Electric Company

Inventor： Rakesh Mullick ,  Vivek Prabhakar Vaidya ,  Fei Zhao ,  Xiaoxing Li ,  Vidya Kamath ,  Kunlin Cao ,  Soma Biswas

IPC: A61B8/08 ,  A61B8/14 ,  A61B8/00 ,  G06T7/00 ,  G06T7/40

CPC classification number: A61B8/5223 ,  A61B8/0825 ,  A61B8/085 ,  A61B8/14 ,  A61B8/4483 ,  G06T7/0012 ,  G06T7/13 ,  G06T7/44 ,  G06T2207/10132 ,  G06T2207/30068 ,  G06T2207/30096

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring an ultrasound image, generating a Fisher-tippett (FT) distribution-based edge feature map from the acquired ultrasound image, generating gradient concentration (GC) scores for pixels of the acquired ultrasound image using the FT distribution-based edge feature map, and identifying a candidate lesion region within the acquired ultrasound image based on the GC scores.

Abstract translation: 提供了一种用于检测超声图像中的损伤的方法。 该方法包括获取超声图像，从获取的超声图像生成基于Fisher-tippett（FT）分布的边缘特征图，使用基于FT分布的边缘特征产生所获取的超声图像的像素的梯度浓度（GC）分数 基于GC分数绘制并识别所获取的超声图像内的候选病变区域。

公开(公告)号：US20150282782A1

公开(公告)日：2015-10-08

申请号：US14247265

申请日：2014-04-08

Applicant: General Electric Company

Inventor： Fei Zhao ,  Vivek Prabhakar Vaidya ,  Rakesh Mullick ,  Soma Biswas ,  Chuyang Ye

IPC: A61B8/08

CPC classification number: A61B8/0825 ,  A61B8/483 ,  A61B8/5207 ,  A61B8/5223 ,  A61B8/5269 ,  G06F19/321 ,  G06T7/0012 ,  G06T7/12 ,  G06T2207/10136 ,  G06T2207/20116 ,  G06T2207/30068 ,  G16H50/20

Abstract: A method for detecting a lesion in an anatomical region of interest is presented. The method includes identifying one or more candidate mass regions in each of a plurality of 3D ultrasound images acquired at different view angles from the anatomical region of interest. Single-view features corresponding to each candidate mass region are identified. For a candidate mass region, a similarity metric between the single-view features corresponding to the candidate mass region and the single-view features corresponding to the other candidate mass regions is determined. The candidate mass region is classified based at least on the similarity metric. A system for imaging and a non-transitory computer readable media for detection of the lesion are also presented.

Abstract translation: 提出了一种用于检测感兴趣的解剖学区域中的病变的方法。 该方法包括识别从与感兴趣的解剖区域不同的视角获取的多个3D超声图像中的每一个中的一个或多个候选质量区域。 识别与每个候选质量区域对应的单视图特征。 对于候选质量区域，确定对应于候选质量区域的单视图特征与对应于其他候选质量区域的单视图特征之间的相似性度量。 至少基于相似性度量对候选质量区进行分类。 还提出了用于成像的系统和用于检测病变的非暂时计算机可读介质。

公开(公告)号：US20150087982A1

公开(公告)日：2015-03-26

申请号：US14033452

申请日：2013-09-21

Applicant: General Electric Company

Inventor： Rakesh Mullick ,  Pavan Kumar Veerabhadra Annangi ,  Xiaoxing Li ,  Vidya Pundalik Kamath ,  Fei Zhao ,  Vivek Prabhakar Vaidya ,  Soma Biswas

IPC: A61B8/08 ,  G06T7/00

CPC classification number: A61B8/5223 ,  A61B8/085 ,  G06T7/0012 ,  G06T7/41 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/30068 ,  G06T2207/30096

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring ultrasound information, determining discriminative descriptors that describe the texture of a candidate lesion region, and classifying each of the discriminative descriptors as one of a top boundary pixel, a lesion interior pixel, a lower boundary pixel, or a normal tissue pixel. The method also includes determining a pattern of transitions between the classified discriminative descriptors, and classifying the candidate lesion region as a lesion or normal tissue based on the pattern of transitions between the classified discriminative descriptors.

Abstract translation: 提供了一种用于检测超声图像中的损伤的方法。 该方法包括获取超声信息，确定描述候选病变区域的纹理的区分描述符，并将每个鉴别描述符分类为顶边界像素，病变内部像素，下边界像素或正常组织像素之一 。 该方法还包括确定分类的辨别描述符之间的转换模式，并且基于分类的识别描述符之间的转换模式将候选病变区域划分为病变或正常组织。