patents.google.com

He et al., 2010 - Google Patents

  • ️Fri Jan 01 2010
Local and global Gaussian mixture models for hematoxylin and eosin stained histology image segmentation

He et al., 2010

View PDF
Document ID
2641231228796278521
Author
Long L
Antani S
Thoma G
Publication year
2010
Publication venue
2010 10th International Conference on Hybrid Intelligent Systems

External Links

Snippet

This paper presents a new algorithm for hematoxylin and eosin (H&E) stained histology image segmentation. With both local and global clustering, Gaussian mixture models (GMMs) are applied sequentially to extract tissue constituents such as nuclei, stroma, and …

Continue reading at www.softcomputing.net (PDF) (other versions)
  • 238000003709 image segmentation 0 title abstract description 14

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/0014—Pre-processing, e.g. image segmentation ; Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/00147—Matching; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20156—Automatic seed setting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/20—Image acquisition
    • G06K9/34—Segmentation of touching or overlapping patterns in the image field
    • G06K9/342—Cutting or merging image elements, e.g. region growing, watershed, clustering-based techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints

Similar Documents

Publication Publication Date Title
Joseph et al. 2022 Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer)
EP2391887B1 (en) 2020-06-17 Image-based risk score-a prognostic predictor of survival and outcome from digital histopathology
Arevalo et al. 2014 Histopathology image representation for automatic analysis: A state-of-the-art review
He et al. 2012 Histology image analysis for carcinoma detection and grading
US20190042826A1 (en) 2019-02-07 Automatic nuclei segmentation in histopathology images
Gunduz-Demir et al. 2010 Automatic segmentation of colon glands using object-graphs
Nguyen et al. 2012 Prostate cancer detection: Fusion of cytological and textural features
Tosun et al. 2010 Graph run-length matrices for histopathological image segmentation
Lu et al. 2015 Automated analysis and diagnosis of skin melanoma on whole slide histopathological images
Lu et al. 2013 Automated segmentation of the melanocytes in skin histopathological images
He et al. 2022 A review: The detection of cancer cells in histopathology based on machine vision
AlZubaidi et al. 2017 Computer aided diagnosis in digital pathology application: Review and perspective approach in lung cancer classification
Xu et al. 2015 Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm
Nateghi et al. 2017 Maximized inter-class weighted mean for fast and accurate mitosis cells detection in breast cancer histopathology images
Alheejawi et al. 2020 Deep learning-based histopathological image analysis for automated detection and staging of melanoma
Kulikova et al. 2012 Nuclei extraction from histopathological images using a marked point process approach
Zhou et al. 2005 Towards automated cellular image segmentation for RNAi genome-wide screening
Saxena et al. 2019 Study of Computerized Segmentation & Classification Techniques: An Application to Histopathological Imagery
He et al. 2010 Local and global Gaussian mixture models for hematoxylin and eosin stained histology image segmentation
He et al. 2011 Multiphase level set model with local K-means energy for histology image segmentation
Han et al. 2020 Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens
WO2014006421A1 (en) 2014-01-09 Identification of mitotic cells within a tumor region
Shirazi et al. 2018 Automated pathology image analysis
Turkki et al. 2016 Identification of immune cell infiltration in hematoxylin-eosin stained breast cancer samples: texture-based classification of tissue morphologies
Mazo et al. 2016 Automatic recognition of fundamental tissues on histology images of the human cardiovascular system