公开(公告)号：US09747683B2

公开(公告)日：2017-08-29

申请号：US14977253

申请日：2015-12-21

Applicant: General Electric Company

Inventor： Arun Karthi Subramaniyan ,  Khan Mohamed Khirullah Genghis Khan ,  Clifford Bueno ,  Ali Can ,  Adrian Gabriel Loghin ,  Jie Yu

IPC: G06T7/00 ,  G06F17/50 ,  G06K9/46 ,  G06K9/62 ,  G06K9/00 ,  G06T5/50

CPC classification number: G06T7/001 ,  G06K9/00208 ,  G06K9/6212 ,  G06K9/6214 ,  G06K2209/19 ,  G06T7/11 ,  G06T2207/10081 ,  G06T2207/30164

Abstract: A monitoring system for determining component wear is provided. The monitoring system includes a memory device configured to store a reference model of a component and a component wear monitoring (CWM) device configured to receive a component image of a first component being inspected, detect a plurality of manmade structural features in the received component image, adjust the component image to mask out at least some of the plurality of manmade structural features from the received component image, compare the adjusted component image with the reference model to determine one or more potential defect areas in the first component, analyze each of the one or more defect areas to determine a state of the potential defect areas, and output the state of the one or more potential defect areas to a user.

公开(公告)号：US20170091964A1

公开(公告)日：2017-03-30

申请号：US14985702

申请日：2015-12-31

Applicant: General Electric Company

Inventor： Jiajia Luo ,  Bruno Kristiaan Bernard De Man ,  Ali Can ,  Eri Haneda

IPC: G06T11/00 ,  G06K9/62

CPC classification number: G06T11/006 ,  G06K9/4642 ,  G06K9/6244 ,  G06K9/6255 ,  G06K9/6256 ,  G06K2209/051 ,  G06T11/008 ,  G06T2210/41 ,  G06T2211/421 ,  G06T2211/424

Abstract: A computationally efficient dictionary learning-based term is employed in an iterative reconstruction framework to keep more spatial information than two-dimensional dictionary learning and require less computational cost than three-dimensional dictionary learning. In one such implementation, a non-local regularization algorithm is employed in an MBIR context (such as in a low dose CT image reconstruction context) based on dictionary learning in which dictionaries from different directions (e.g., x,y-plane, y,z-plane, x,z-plane) are employed and the sparse coefficients calculated accordingly. In this manner, spatial information from all three directions is retained and computational cost is constrained.

公开(公告)号：US08995740B2

公开(公告)日：2015-03-31

申请号：US13865036

申请日：2013-04-17

Applicant: General Electric Company

Inventor： Alberto Santamaria-Pang ,  Jens Rittscher ,  Dirk Padfield ,  Ali Can ,  Zhengyu Pang ,  Musodiq Bello ,  Fiona Ginty ,  Christopher Sevinsky ,  Qing Li ,  Megan Rothney ,  Brion Sarachan

IPC: G06K9/00 ,  G06T7/00 ,  G01N33/574

CPC classification number: G06K9/0014 ,  G06T7/0012 ,  G06T2207/10064 ,  G06T2207/30024

Abstract: Improved systems and methods for the analysis of digital images are provided. More particularly, the present disclosure provides for improved systems and methods for the analysis of digital images of biological tissue samples. Exemplary embodiments provide for: i) segmenting, ii) grouping, and iii) quantifying molecular protein profiles of individual cells in terms of sub cellular compartments (nuclei, membrane, and cytoplasm). The systems and methods of the present disclosure advantageously perform tissue segmentation at the sub-cellular level to facilitate analyzing, grouping and quantifying protein expression profiles of tissue in tissue sections globally and/or locally. Performing local-global tissue analysis and protein quantification advantageously enables correlation of spatial and molecular configuration of cells with molecular information of different types of cancer.

Abstract translation: 提供了改进的数字图像分析系统和方法。 更具体地，本公开提供了用于分析生物组织样本的数字图像的改进的系统和方法。 示例性实施方案提供：i）分段，ii）分组，和iii）根据亚细胞区室（细胞核，膜和细胞质）量化各细胞的分子蛋白质谱。 本公开的系统和方法有利地在亚细胞水平进行组织分割，以便于全局和/或局部地分析，分组和定量组织部位中组织的蛋白质表达谱。 进行局部全局组织分析和蛋白质定量有利于使细胞的空间和分子构型与不同类型癌症的分子信息的相关性。

公开(公告)号：US20140177390A1

公开(公告)日：2014-06-26

申请号：US13722755

申请日：2012-12-20

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Ali Can ,  Erhan Bas

IPC: G01V1/30

CPC classification number: G01V1/302 ,  G01V1/28 ,  G01V1/30 ,  G01V1/301 ,  G01V1/345 ,  G01V2210/64

Abstract: An approach for seismic data analysis is provided. In accordance with embodiments of this approach, parallel regions within a volume of seismic data are modeled. Residual regions within the volumetric data set are identified, where the residual regions comprise those regions not modeled as parallel regions. The residual regions or a graphic derived from the residual regions are displayed for review.

Abstract translation: 提供了一种地震数据分析方法。 根据该方法的实施例，对地震数据量的平行区域进行建模。 识别体积数据集内的剩余区域，其中残余区域包括未被建模为平行区域的那些区域。 显示剩余区域或从残差区域导出的图形以供查看。