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

scTenifoldKnk: An efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation.

Patterns (New York, N.Y.)·2022

Same author

scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data.

Patterns (New York, N.Y.)·2020

Same author

Single-Cell Expression Variability Implies Cell Function.

Cells·2019

Same author

Estimation of a Semiparametric Varying-Coefficient Mixed Regressive Spatial Autoregressive Model.

Econometrics and statistics·2019

Same author

Exponential Family Functional data analysis via a low-rank model.

Biometrics·2018

Same author

Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions.

Computational and structural biotechnology journal·2017

Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 8, 2026

Rapid Nanoprobe Signal Enhancement by In Situ Gold Nanoparticle Synthesis

Rapid Nanoprobe Signal Enhancement by In Situ Gold Nanoparticle Synthesis

Published on: March 7, 2018

Robust Nanoparticles Detection From Noisy Background by Fusing Complementary Image Information.

Yanjun Qian, Jianhua Z Huang, Xiaodong Li

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 24, 2017

Summary

This summary is machine-generated.

This study introduces a robust method for detecting nanoparticles in noisy Transmission Electron Microscopic (TEM) images. The approach fuses pixel intensity and gradient information to accurately identify and model nanoparticles.

More Related Videos

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy

Published on: May 16, 2022

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

Related Experiment Videos

Last Updated: Mar 8, 2026

Rapid Nanoprobe Signal Enhancement by In Situ Gold Nanoparticle Synthesis

Rapid Nanoprobe Signal Enhancement by In Situ Gold Nanoparticle Synthesis

Published on: March 7, 2018

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy

Published on: May 16, 2022

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

Area of Science:

Materials Science
Image Analysis
Nanotechnology

Background:

Transmission Electron Microscopy (TEM) is crucial for nanoparticle characterization.
Noisy and low-contrast TEM images pose challenges for accurate nanoparticle detection.
Existing methods may struggle with image quality variations.

Purpose of the Study:

To develop a robust method for detecting nanoparticles in noisy TEM images.
To accurately fit detected nanoparticles using an elliptic shape model.
To enhance detection accuracy by fusing complementary image information.

Main Methods:

Proposed a novel approach fusing pixel intensity and gradient information.
Implemented a two-step process: independent data processing and conflict resolution.
Utilized binary integer programming (BIP) for robust nanoparticle identification.

Main Results:

Successfully detected and fitted nanoparticles in TEM images with varying resolutions and noise levels.
Demonstrated robustness against low contrast and high noise.
Achieved efficient processing of 1024x1024 pixel images within minutes.

Conclusions:

The proposed fusion method significantly improves nanoparticle detection in challenging TEM images.
The BIP-based conflict resolution ensures reliable identification.
The method offers a robust and efficient solution for nanoparticle analysis in TEM.