Hypersensitivity Diagnosis. Development of an Automatic and Quantitative IgE Concentration Detection Algorithm

Inhaltsverzeichnis (Table of Contents)

• Abstract
• Chapter 1 Introduction
• 1.1 Background
• 1.2 Prevalence of Allergic Diseases
• 1.3 Current Methods of Allergy Diagnosis
• 1.4 Aim of the Study
• Chapter 2 Materials and Methods
• 2.1 Materials
• 2.2 Methods
• Chapter 3 Results and Analysis
• 3.1 Gray Value vs IgE Concentration
• 3.2 Multi Spectral Area vs IgE Concentration
• 3.3 Color Difference vs IgE Concentration
• Chapter 4 Discussion
• 4.1 Effect of System Environment
• 4.2 Comparison of G Channel in Wet and Dry Conditions
• 4.3 Comparison of Luma Intensity in Wet and Dry Conditions
• Chapter 5 Conclusion

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This dissertation aims to develop an automatic and quantitative IgE concentration detection algorithm for use in a self-designed allergen disease diagnosis system. It focuses on improving the accuracy and efficiency of allergy diagnosis through image processing techniques applied to ELISA results.

• Development of an automated IgE concentration detection algorithm
• Utilization of image processing techniques for allergy diagnosis
• Evaluation of the impact of system environment on IgE detection
• Quantitative analysis of IgE concentration based on image features
• Optimization of the diagnostic system for accuracy and cost-effectiveness

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1 introduces the prevalence of allergic diseases and discusses current methods of allergy diagnosis, highlighting their limitations. The chapter outlines the aim of the study, which focuses on developing a new, automated and quantitative IgE detection algorithm for a self-designed allergen disease diagnosis system. Chapter 2 details the materials and methods used in the study, including the ELISA technique and image processing techniques. Chapter 3 presents the results and analysis of the experiments, focusing on the relationship between IgE concentration and different image features. Chapter 4 discusses the findings, particularly the effect of system environment on IgE detection and the comparison of various image features in different color models.

Schlüsselwörter (Keywords)

This study explores the use of image processing techniques for quantitative IgE detection in allergen disease diagnosis. The primary keywords are Allergic disease, Automatic Diagnosis, IgE, ELISA, and Quantitative Detection. The study utilizes various color models and image features to analyze the relationship between IgE concentration and the resulting images, aiming to create a more accurate and efficient diagnostic system.