[go: up one dir, main page]

Roszkowiak et al., 2015 - Google Patents

Short survey: Adaptive threshold methods used to segment immunonegative cells from simulated images of follicular lymphoma stained with 3, 3 …

Roszkowiak et al., 2015

View PDF
Document ID
16109388509401281351
Author
Roszkowiak L
Korzynska A
Pijanowska D
Publication year
Publication venue
2015 Federated Conference on Computer Science and Information Systems (FedCSIS)

External Links

Snippet

We perform a short survey of image thresholding methods for very specific task, and assess their performance comparison. We analyse performance of adaptive thresholding methods concerning segmentation of immunonegative cells of follicular lymphoma tissue samples …
Continue reading at annals-csis.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • G06T2207/30024Cell structures in vitro; Tissue sections in vitro
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/00147Matching; Classification
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10Investigating individual particles
    • G01N15/14Electro-optical investigation, e.g. flow cytometers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image

Similar Documents

Publication Publication Date Title
US12094105B2 (en) System and method for automatic labeling of pathology images
Das et al. Classifying histopathology whole-slides using fusion of decisions from deep convolutional network on a collection of random multi-views at multi-magnification
JP6791245B2 (en) Image processing device, image processing method and image processing program
Korzynska et al. Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3, 3’-Diaminobenzidine&Haematoxylin
WO2008005426A2 (en) Computer-aided pathological diagnosis system
JP2022506135A (en) Segmentation of 3D intercellular structures in microscopic images using iterative deep learning flows that incorporate human contributions
JPH09509487A (en) Cell sample automatic classification device and method
JP4383352B2 (en) Histological evaluation of nuclear polymorphism
Chen et al. An accurate and universal approach for short-exposure-time microscopy image enhancement
Lyashenko et al. Using the methodology of wavelet analysis for processing images of cytology preparations
Stojić et al. Classification by morphology of multipolar neurons of the human principal olivary nucleus
Roszkowiak et al. Short survey: Adaptive threshold methods used to segment immunonegative cells from simulated images of follicular lymphoma stained with 3, 3′-Diaminobenzidine&Haematoxylin
KR20210079134A (en) Multi-features classification of prostate carcinoma observed in histological sections
Battula et al. Automatic classification of Non Hodgkin‘s lymphoma using histological images: recent advances and directions
Cetin et al. Deep learning-based restaining of histopathological images
Neuman et al. Segmentation of stained lymphoma tissue section images
Roszkowiak et al. Improvements to segmentation method of stained lymphoma tissue section images
Parvaze et al. Extraction of multiple cellular objects in HEp-2 images using LS segmentation
Guo et al. An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images
Beevi et al. Analysis of nuclei detection with stain normalization in histopathology images
Fernandez-Gonzalez et al. Quantitative image analysis in mammary gland biology
Blackledge et al. Targeting cell nuclei for the automation of Raman spectroscopy in cytology
Korzyńska et al. The method of immunohistochemical images standardization
Kazmar et al. Learning cellular texture features in microscopic cancer cell images for automated cell-detection
Sindhoora et al. Machine learning aided classification and grading of biopsy sample using discrete wavelet transform and gray level co-occurrence matrix