Lyashenko et al., 2017 - Google Patents
The study of blood smear as the analysis of images of various objectsLyashenko et al., 2017
View PDF- Document ID
- 8591003253999545055
- Author
- Lyashenko V
- Babker A
- Lyubchenko V
- Publication year
External Links
Snippet
Анотація Processing of microscope images in medicine is one of the priority research areas. Among the many medical imaging follows allocate the image of blood preparations. This is due to the fact that study of the image of blood preparations allows to conduct a …
- 210000004369 Blood 0 title abstract description 67
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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Electro-optical investigation, e.g. flow cytometers
- G01N15/1468—Electro-optical investigation, e.g. flow cytometers with spatial resolution of the texture or inner structure of the particle
- G01N15/1475—Electro-optical investigation, e.g. flow cytometers with spatial resolution of the texture or inner structure of the particle using image analysis for extracting features of the particle
-
- 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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Electro-optical investigation, e.g. flow cytometers
- G01N15/1456—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
- G01N15/1459—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals the analysis being performed on a sample stream
-
- 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
-
- 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
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- 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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
-
- 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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N2015/0065—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials biological, e.g. blood
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Merino et al. | Optimizing morphology through blood cell image analysis | |
Dese et al. | Accurate machine-learning-based classification of leukemia from blood smear images | |
US4175860A (en) | Dual resolution method and apparatus for use in automated classification of pap smear and other samples | |
Chadha et al. | An automated method for counting red blood cells using image processing | |
Wu et al. | A hematologist-level deep learning algorithm (BMSNet) for assessing the morphologies of single nuclear balls in bone marrow smears: algorithm development | |
CA2130338C (en) | Method for identifying normal biomedical specimens | |
Hortinela et al. | Identification of abnormal red blood cells and diagnosing specific types of anemia using image processing and support vector machine | |
Khobragade et al. | Detection of leukemia in microscopic white blood cell images | |
Liu et al. | Bone marrow cells detection: a technique for the microscopic image analysis | |
Lyashenko et al. | Contrast modification as a tool to study the structure of blood components | |
Horn et al. | Performance of the CellaVision® DM96 system for detecting red blood cell morphologic abnormalities | |
Lyashenko et al. | Using the methodology of wavelet analysis for processing images of cytology preparations | |
Mohammed et al. | Automatic Cytoplasm and Nucleus detection in the white blood cells depending on hisogram analysis | |
Nikitaev et al. | The blood smear image processing for the acute leukemia diagnostics | |
Evangeline et al. | Computer aided system for human blood cell identification, classification and counting | |
Babker et al. | Identification of megaloblastic anemia cells through the use of image processing techniques | |
KR101106386B1 (en) | System for classifying slides using scatter plot distributions | |
Khongjaroensakun et al. | Retracted: White blood cell differentials performance of a new automated digital cell morphology analyzer: Mindray MC‐80 | |
Lyashenko et al. | The study of blood smear as the analysis of images of various objects | |
Marzec et al. | Efficient automatic 3D segmentation of cell nuclei for high-content screening | |
Maggavi et al. | Motility analysis with morphology: Study related to human sperm | |
US10303923B1 (en) | Quantitation of NETosis using image analysis | |
Al-Momin et al. | A MATLAB model for diagnosing sickle cells and other blood abnormalities using image processing | |
Jiang et al. | Performance of the digital cell morphology analyzer MC-100i in a multicenter study in tertiary hospitals in China | |
Yu et al. | A machine-learning-based algorithm for bone marrow cell differential counting |