Gil et al., 2022 - Google Patents
Automatic analysis system for abnormal red blood cells in peripheral blood smearsGil et al., 2022
- Document ID
- 6294766232294593472
- Author
- Gil T
- Moon C
- Lee S
- Lee O
- Publication year
- Publication venue
- Microscopy Research and Technique
External Links
Snippet
The type and ratio of abnormal red blood cells (RBCs) in blood can be identified through peripheral blood smear test. Accurate classification is important because the accompanying diseases indicated by abnormal RBCs vary. In clinical practice, this task is time‐consuming …
- 210000003743 Erythrocytes 0 title abstract description 232
Classifications
-
- 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
- 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
- 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
-
- 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
- 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
- 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
- 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
- G06T2207/10—Image acquisition modality
-
- 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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rodellar et al. | Image processing and machine learning in the morphological analysis of blood cells | |
Ghaderzadeh et al. | Machine learning in detection and classification of leukemia using smear blood images: a systematic review | |
Ghahremani et al. | Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification | |
Rehman et al. | Classification of acute lymphoblastic leukemia using deep learning | |
Simon et al. | Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images | |
MoradiAmin et al. | Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis | |
US10275880B2 (en) | Image processing method and system for analyzing a multi-channel image obtained from a biological tissue sample being stained by multiple stains | |
CN113454733A (en) | Multi-instance learner for prognostic tissue pattern recognition | |
Shahzad et al. | Robust Method for Semantic Segmentation of Whole‐Slide Blood Cell Microscopic Images | |
Molina et al. | Sequential classification system for recognition of malaria infection using peripheral blood cell images | |
Kowal et al. | Breast cancer nuclei segmentation and classification based on a deep learning approach | |
Casiraghi et al. | A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections | |
AU2021349226C1 (en) | Critical component detection using deep learning and attention | |
WO2022066725A1 (en) | Training end-to-end weakly supervised networks in a multi-task fashion at the specimen (supra-image) level | |
Delpiano et al. | Automated detection of fluorescent cells in in‐resin fluorescence sections for integrated light and electron microscopy | |
Nobile et al. | Unsupervised neural networks as a support tool for pathology diagnosis in MALDI-MSI experiments: A case study on thyroid biopsies | |
MoradiAmin et al. | Automatic classification of acute lymphoblastic leukemia cells and lymphocyte subtypes based on a novel convolutional neural network | |
Marcuzzo et al. | Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging | |
Hortinela IV et al. | Development of abnormal red blood cells classifier using image processing techniques with support vector machine | |
Gil et al. | Automatic analysis system for abnormal red blood cells in peripheral blood smears | |
Wilm et al. | Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification | |
Khan et al. | An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images | |
Ur Rahman et al. | Efficient segmentation of lymphoblast in acute lymphocytic leukemia | |
Saxena et al. | Review of computer‐assisted diagnosis model to classify follicular lymphoma histology | |
Reta et al. | Leukocytes segmentation using Markov random fields |