Invention Grant
US08488863B2 Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials
有权
组合逐像素和对象级分类,分段和聚集进行定量图像分析,区分健康的非癌细胞和癌细胞核,并描绘染色的生物组织材料中的核,细胞质和基质物质
- Patent Title: Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials
- Patent Title (中): 组合逐像素和对象级分类,分段和聚集进行定量图像分析,区分健康的非癌细胞和癌细胞核,并描绘染色的生物组织材料中的核,细胞质和基质物质
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Application No.: US12266450Application Date: 2008-11-06
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Publication No.: US08488863B2Publication Date: 2013-07-16
- Inventor: Laura E. Boucheron
- Applicant: Laura E. Boucheron
- Applicant Address: US NM Los Alamos
- Assignee: Los Alamos National Security, LLC
- Current Assignee: Los Alamos National Security, LLC
- Current Assignee Address: US NM Los Alamos
- Agent John P. O'Banion
- Main IPC: G06K9/469
- IPC: G06K9/469 ; G06T7/12

Abstract:
Quantitative object and spatial arrangement-level analysis of tissue are detailed using expert (pathologist) input to guide the classification process. A two-step method is disclosed for imaging tissue, by classifying one or more biological materials, e.g. nuclei, cytoplasm, and stroma, in the tissue into one or more identified classes on a pixel-by-pixel basis, and segmenting the identified classes to agglomerate one or more sets of identified pixels into segmented regions. Typically, the one or more biological materials comprises nuclear material, cytoplasm material, and stromal material. The method further allows a user to markup the image subsequent to the classification to re-classify said materials. The markup is performed via a graphic user interface to edit designated regions in the image.
Public/Granted literature
- US20100111396A1 OBJECT AND SPATIAL LEVEL QUANTITATIVE IMAGE ANALYSIS Public/Granted day:2010-05-06
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