Allergic diseases are seen to affect a large number of people around the world every year. The proper diagnosis of allergic disease is an important factor to maintain the good health condition of a general population. Although there are several techniques available for the diagnosis of allergic disease, most of the methods fail to cover all of the criteria to make it acceptable.
This study introduces general methods of allergy diagnosis which are in practice in current scenario and different image processing techniques that can be incorporated into it to make the process even easier. In this study ELISA technique is proposed to fulfill all the basic criteria like sensitivity, accuracy and most importantly cost effectiveness. ELISA is an immunoassay that involves the detection of Immunoglobulin type E commonly referred as IgE. An enzyme linked anti-human globulin is made to react with IgE, if present in the patient's serum which will produce the colored product when the substrate of the enzyme is added. Depending upon the concentration of the colored product, the concentration of IgE is determined. The experiment condition like wet condition and plastic container can significantly change the detected color of the allergen card and hence the result value. This proves that the system environment indeed affects calculated IgE concentration. In order to reduce this negative effect, we need to find an effective image feature which is little influenced by system environment. The content of this study mainly focuses on different color models of the color images that can be used in image processing technology which are further utilized to define its relationship with IgE concentration or the severity of the disease. Involving the features of both binary and color image models it gives the better comparison of the different image features in different color models. These image features ware tested and result curves were produced that describes the relationship between IgE concentration and image feature. Based on the experimental findings the new diagnostic image feature was introduced. The experiments were done to test the accuracy of the proposed diagnostic image feature which is cheap, sensitive and can detect the IgE concentration quantitatively.
Table of Contents
CHAPTER-1 Background
1.1 Introduction
1.2 Diagnosis of Allergic Diseases
1.3 Research Status
1.4 In-vitro Allergy Test Products
CHAPTER 2 In-Vitro Allergen Testing Device
2.1 Preparation of Reagent
2.2 Experimental Device
2.3 System Assembly
Chapter 3 Verification of System Stability and Grayscale Image Analysis
3.1 Introduction
3.2 Color calibration of the imaging system by intensity ratio analysis
3.3 Gray Value Analysis of Colored Reaction Product
CHAPTER 4 COLOR MODELS AND IMAGE ANALYSIS
4.1 Introduction
4.2 Color Models
5. Polynomial Regression
5.1 Background
5.2 Null hypothesis
5.3 Experiment Motive
5.4 Procedure Details
6. Summary and Future Work
7. References
Research Objectives and Themes
This thesis aims to develop an automatic and quantitative IgE concentration detection algorithm for use in a self-designed allergen disease diagnosis system. By utilizing image processing techniques on ELISA reagent strips, the research seeks to create a cost-effective, sensitive, and reproducible method for allergy diagnosis that overcomes the limitations of manual qualitative assessments.
- Development of an automated quantitative image analysis method for IgE concentration.
- Evaluation of various color models (RGB, YUV, YCbCr, HSV, HSL) for image feature extraction.
- Implementation of polynomial regression models to define the relationship between IgE levels and image features.
- Mitigation of environmental noise and system errors in image acquisition.
Excerpt from the Book
3.3.4.5 Color Difference Signal
Considering the color of the reagent strip is green and blue in white light then the green-blue color of the allergen card tends to be higher with high IgE concentration. Higher the concentration of IgE, greater will be the contribution of the green and blue channel. So at the higher IgE concentration, scanning of the allergen block at single wavelength may be difficult to separate different color products from one another. Hence, it is expected to use B and G signal channel and the difference signal channel. For the analysis of the cat allergen data, G channel wavelength is around 530-550 nm and the B channel wavelength is around 430-450 nm. The G channel is set to value a, and the B channel to value b.
The color difference at the characteristic signal value, denoted by T is given by the formula T = b*X*a, where X is the color coefficient. Color difference results in a high concentration (21.09 ~ 60.27IU/ml) is monotonically increasing with highest sensitivity, expressed as the maximum slope. Then b*x*a formula is extracted. Experimental results show that, x = 5.5, a is 430nm, b is the maximum slope of the color difference signal 550nm. Inversely, for the low concentration IgE sample light wavelength 610 nm is sensitive. So for the low IgE concentration sample, characteristic signal value is estimated at 610 nm wavelength light.
Summary of Chapters
CHAPTER-1 Background: This chapter provides an overview of allergic diseases, their global prevalence, and current diagnostic methodologies including Skin Prick Tests and ELISA.
CHAPTER 2 In-Vitro Allergen Testing Device: This section details the experimental design, including the preparation of reagent strips and the assembly of the imaging system.
Chapter 3 Verification of System Stability and Grayscale Image Analysis: This chapter covers the calibration of the imaging setup to ensure system stability and introduces the processing of grayscale images for data collection.
CHAPTER 4 COLOR MODELS AND IMAGE ANALYSIS: This chapter investigates various color models such as RGB, YUV, and YCbCr to identify optimal features for quantifying IgE concentration.
5. Polynomial Regression: This chapter discusses the use of polynomial regression models to establish a reliable relationship between independent IgE concentration variables and dependent image feature responses.
6. Summary and Future Work: This chapter concludes the research by summarizing the developed methods and proposing directions for future improvements in automated allergy diagnostics.
Keywords
Allergic disease, Automatic Diagnosis, IgE, ELISA, Quantitative Detection, Image Processing, Color Models, Polynomial Regression, Reagent Strip, Allergen, Multispectral Imaging, G Channel, Machine Vision, Sensitivity, Immunoassay.
Frequently Asked Questions
What is the primary focus of this research?
The research focuses on developing an automatic and quantitative tool for measuring IgE concentration to improve the diagnosis of allergic diseases using image processing.
What diagnostic technique is central to this study?
The study utilizes the Enzyme-linked Immuno-Sorbent Assay (ELISA) technique performed on paper reagent strips as the basis for color-based detection.
What is the main objective of using image processing here?
The objective is to replace manual qualitative observation with an accurate, quantitative, and reproducible automated system for analyzing IgE-induced color intensity.
Which scientific methodology is applied for data analysis?
The research employs multispectral imaging, image feature extraction from various color models, and polynomial regression to map image features to IgE concentration levels.
What does the main body of the work cover?
It covers the experimental hardware setup, calibration procedures, the evaluation of different color spaces, and the development of regression curves to quantify serum IgE.
What are the characterizing keywords of the work?
Key terms include Allergic disease, Automatic Diagnosis, IgE, ELISA, Quantitative Detection, Image Processing, and Polynomial Regression.
How does the system account for system environment variables?
The study investigates the impact of wet versus dry conditions on color intensity and explores image features and algorithms to compensate for these environmental differences.
Why are multiple color models evaluated in the study?
Different color models are analyzed to find an image feature that is monotonic, sensitive, and stable across various environmental conditions to ensure reliable quantification.
- Quote paper
- Satish Thapaliya (Author), 2014, Hypersensitivity Diagnosis. Development of an Automatic and Quantitative IgE Concentration Detection Algorithm, Munich, GRIN Verlag, https://www.grin.com/document/506858
