In this study, we present a novel statistical thresholding approach specifically designed for the analysis of test cells. Traditional thresholding techniques often rely on fixed or heuristic parameters that may not adequately capture the variability and intricacies present in diverse datasets. Our method leverages detailed statistical analysis of test cell data to dynamically determine optimal threshold values, enhancing the accuracy and reliability of cell detection and analysis. We begin by examining the statistical properties of test cell datasets, identifying key metrics that influence thresholding performance. Using these insights, we develop a robust algorithm that adjusts threshold levels based on real-time statistical feedback from the test cell population.

This approach is validated through extensive experimentation on a variety of datasets, demonstrating significant improvements in both precision and recall compared to conventional methods. The results indicate that our statistical thresholding technique not only adapts to different data conditions but also reduces the incidence of false positives and negatives. By integrating this method into existing analysis workflows, researchers and practitioners can achieve more accurate and consistent results in test cell analysis, paving the way for advancements in fields such as biomedical research, materials science, and quality control.

Statistical Thresholding, Test Cell Analysis, Dynamic Thresholding

Sebasti´an A. Villar,“A Framework for Acoustic Segmentation Using Order Statistic-Constant False Alarm Rate in Two Dimensions From Sidescan Sonar Data",IEEE International Conference on Advances in Communication and signal processing IEEE 20017.

Tri-Tan Van Cao, “A CFAR Thresholding Approach Based on Test Cell Statistics’’, IEEE International Conference on Advances in Communication and signal processing IEEE 2004.

Hermann Rohling ,“ Radar CFAR Thresholding in Clutter and Multiple Target Situations", IEEE International conference IEEE 2002.

M. E. Smith,P.K. Varshney, “Intelligent CFAR Processor Based on Data Variability",IEEE International Conference on Advances in Communication and signal processing IEEE 2000

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