Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Predicting allergy and postpartum depression from an incomplete compositional microbiome.

BMC genomics·2025

Same author

Rapid label-free identification of seven bacterial species using microfluidics, single-cell time-lapse phase-contrast microscopy, and deep learning-based image and video classification.

PloS one·2025

Same author

Culture-free detection of bacteria from blood for rapid sepsis diagnosis.

NPJ digital medicine·2025

Same author

Discovery of tumour indicating morphological changes in benign prostate biopsies through AI.

Scientific reports·2025

Same author

Streamlining Multiplexed Tissue Image Analysis with PIPΣX: An Integrated Automated Pipeline for Image Processing and EXploration for Diverse Tissue Types.

bioRxiv : the preprint server for biology·2025

Same author

CombiANT reader: Deep learning-based automatic image processing tool to robustly quantify antibiotic interactions.

PLOS digital health·2025

Same journal

A Modular High-Parameter Flow Cytometry Framework: Pre-Analytical Optimization and Validation for Clinical Research.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

Quantitative Detection of Entotic Cell-In-Cell Structures Using Deformable Segmentation and Deep Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

Comparison of Tissue Preparations to Identify and Phenotype T Cells in Human Colorectal Tumor Tissue.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

Refractive Index-Correlated Pseudocoloring for Adaptive Color Fusion in Holotomographic Cytology.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

Ensembling Unets for Rare Chromosomal Aberration Detection in Metaphase Images, Uncertainty Quantification, and Ionizing Radiation Dose Estimation.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

OMIP-121: Immune Phenotyping of Canine Peripheral Leukocytes by Mass Cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 26, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

Global gray-level thresholding based on object size.

Petter Ranefall¹, Carolina Wählby¹

¹Centre for Image Analysis and SciLifeLab, Uppsala University, Uppsala, Sweden.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|January 23, 2016

Summary

This summary is machine-generated.

This study introduces a fast, robust global gray-level thresholding method for microscopy. It accurately segments objects by considering their size, outperforming existing methods in robustness.

Keywords:

algorithms automated microscopy pattern recognition

More Related Videos

A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis

A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis

Published on: March 9, 2015

Author Spotlight: Unveiling the Molecular Basis of Pain Perception and Neuropathic Pain

Author Spotlight: Unveiling the Molecular Basis of Pain Perception and Neuropathic Pain

Published on: August 9, 2024

Related Experiment Videos

Last Updated: Mar 26, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis

A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis

Published on: March 9, 2015

Author Spotlight: Unveiling the Molecular Basis of Pain Perception and Neuropathic Pain

Author Spotlight: Unveiling the Molecular Basis of Pain Perception and Neuropathic Pain

Published on: August 9, 2024

Area of Science:

Biomedical Imaging
Image Analysis
Computational Biology

Background:

Accurate segmentation of cellular structures in microscopy is crucial for quantitative analysis.
Existing global thresholding methods often struggle with variations in object size and image noise.
Feature-based segmentation offers potential but requires robust algorithms.

Purpose of the Study:

To develop a fast and robust global gray-level thresholding method for biomedical microscopy.
To improve segmentation accuracy by incorporating object size information.
To provide a user-friendly tool for researchers in cell biology and related fields.

Main Methods:

A novel global gray-level thresholding approach utilizing object size constraints.
Implementation based on the component tree representation for efficient computation.
Development of ImageJ and CellProfiler plugins for accessibility.

Main Results:

The proposed method demonstrates superior robustness compared to standard thresholding techniques.
Effective segmentation of cell nuclei and synthetic fluorescent spots was achieved.
The method requires only an expected object size interval as input.

Conclusions:

The object-size-based thresholding method offers a significant advancement in microscopy image analysis.
Its robustness and ease of use make it a valuable tool for biomedical research.
The component tree approach enables efficient and accurate feature-based segmentation.