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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...

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

An image-driven model for pattern detection, resistant to Birdsall linearisation.

Joshua A Solomon¹

¹Centre for Applied Vision Research, City, University of London, EC1V 0HB, United Kingdom.

Vision Research

|October 6, 2022

Summary

This summary is machine-generated.

The Birdsall theorem states noise should linearize detection, but it doesn't. This study modifies an image-driven model to explain curved psychometric functions, suggesting noise arises after sensor combination, not from independent sensors.

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Area of Science:

Visual perception
Computational neuroscience
Psychophysics

Background:

The Birdsall theorem predicts that external noise should linearize the psychometric function (d-prime vs. target amplitude) if detection relies on an isolated transducer.
Empirical evidence shows that noise can elevate detection thresholds without linearizing the psychometric function, contradicting the isolated transducer model.
Existing image-driven models struggle to explain this failure of Birdsall linearization without invoking intrinsic uncertainty, which assumes independent noisy sensors.

Purpose of the Study:

To reconcile the failure of Birdsall linearization with image-driven models of visual detection.
To propose a modification to existing models that can predict curved psychometric functions in the presence of external noise.
To investigate the source of performance-limiting noise in visual detection.

Main Methods:

Modification of the Watson and Solomon (1997) image-driven model by pooling sensor outputs before image comparison.
Testing the modified model's ability to predict psychometric functions with external noise.
Evaluating the model's fit to pattern-masking thresholds in the absence of noise.

Main Results:

The modified model successfully predicts curved psychometric functions even when external noise elevates thresholds by over 20 dB.
The model's fit to pattern-masking data without external noise remains high quality.
The results demonstrate that the failure of Birdsall linearization does not necessitate independent noisy sensors.

Conclusions:

Performance-limiting noise in visual detection may arise after the combination of outputs from mutually inhibitory sensors, rather than from independent sensors.
A simple modification to image-driven models can account for observed psychometric function shapes under noisy conditions.
This challenges the necessity of intrinsic uncertainty in explaining visual detection failures in the presence of noise.