Wong et al., 2017 - Google Patents
Modified FlowCAM procedure for quantifying size distribution of zooplankton with sample recycling capacityWong et al., 2017
View HTML- Document ID
- 15941963315367481775
- Author
- Wong E
- Sastri A
- Lin F
- Hsieh C
- Publication year
- Publication venue
- Plos one
External Links
Snippet
We have developed a modified FlowCAM procedure for efficiently quantifying the size distribution of zooplankton. The modified method offers the following new features: 1) prevents animals from settling and clogging with constant bubbling in the sample container; …
- 238000000034 method 0 title abstract description 35
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/64—Fluorescence; Phosphorescence
-
- 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
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/47—Scattering, i.e. diffuse reflection
- G01N21/49—Scattering, i.e. diffuse reflection within a body or fluid
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/84—Systems specially adapted for particular applications
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sent et al. | Deriving water quality parameters using sentinel-2 imagery: A case study in the Sado Estuary, Portugal | |
Massarelli et al. | A handy open-source application based on computer vision and machine learning algorithms to count and classify microplastics | |
Zhang et al. | White blood cell segmentation by color-space-based k-means clustering | |
Gorsky et al. | Digital zooplankton image analysis using the ZooScan integrated system | |
Dacal et al. | Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection | |
Wong et al. | Modified FlowCAM procedure for quantifying size distribution of zooplankton with sample recycling capacity | |
Park et al. | Single cell analysis of stored red blood cells using ultra-high throughput holographic cytometry | |
Sun et al. | Wood–leaf classification of tree point cloud based on intensity and geometric information | |
Kim et al. | Predictive system implementation to improve the accuracy of urine self-diagnosis with smartphones: application of a confusion matrix-based learning model through RGB semiquantitative analysis | |
Geread et al. | Pinet–an automated proliferation index calculator framework for Ki67 breast cancer images | |
Mondol et al. | Application of high-throughput screening Raman spectroscopy (HTS-RS) for label-free identification and molecular characterization of Pollen | |
Maddalena et al. | Artificial intelligence for cell segmentation, event detection, and tracking for label-free microscopy imaging | |
Pérez-Sanz et al. | Efficiency of machine learning algorithms for the determination of macrovesicular steatosis in frozen sections stained with sudan to evaluate the quality of the graft in liver transplantation | |
Zang et al. | Fast analysis of time-domain fluorescence lifetime imaging via extreme learning machine | |
Nakaguchi et al. | Fast and non-destructive quail egg freshness assessment using a thermal camera and deep learning-based air cell detection algorithms for the revalidation of the expiration date of eggs | |
Liu et al. | On the acquisition of high-quality digital images and extraction of effective color information for soil water content testing | |
Yoo et al. | Turning image sensors into position and time sensitive quantitative colorimetric data sources with the aid of novel image processing/analysis software | |
Bulloni et al. | Automated analysis of proliferating cells spatial organisation predicts prognosis in lung neuroendocrine neoplasms | |
Zhao et al. | Rapid and accurate prediction of soil texture using an image-based deep learning autoencoder convolutional neural network random forest (DLAC-CNN-RF) algorithm | |
Doh et al. | Development of a Smartphone-Integrated Reflective Scatterometer for Bacterial Identification | |
Tharmakulasingam et al. | An artificial intelligence-assisted portable low-cost device for the rapid detection of SARS-CoV-2 | |
Liu et al. | Software Tools for 2D Cell Segmentation | |
Kurnia et al. | Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension | |
Yu et al. | Estimation of a new canopy structure parameter for rice using smartphone photography | |
Zehra et al. | Use of a novel deep learning open-source model for quantification of Ki-67 in breast cancer patients in Pakistan: a comparative study between the manual and automated methods |