Biological Tissue Imaging via Multi-Wavelength Texture Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical imaging techniques are limited in adaptability and often require skilled professionals to distinguish between normal and abnormal biological tissue images, leading to potential diagnosis errors due to ambiguity in visual information.
Innovation Solution
The method involves obtaining images of biological tissue at multiple wavelengths, performing texture analysis using spatial and spectral information, and generating a texture image to classify tissues as normal or abnormal based on first-order statistics, enhancing image clarity and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current imaging techniques are used to detect anomalies in biological tissue, then the imaging process is simple and quick, but the images are ambiguous and require highly skilled professionals for accurate diagnosis
Solution Approach 1:
The patent transitions from two-dimensional visual image analysis to three-dimensional texture analysis by incorporating spectral information as an additional dimension. This allows the system to characterize tissue anomalies through texture features across multiple wavelengths, improving diagnostic accuracy without requiring highly skilled professionals to interpret ambiguous visual images.
Solution Approach 2:
The patent changes the analytical parameters from simple visual inspection to quantitative texture analysis using Gray Level Co-occurrence Matrix (GLCM) features. By extracting statistical parameters such as contrast, correlation, energy, and homogeneity from multi-wavelength images, the system transforms subjective visual assessment into objective, automated measurement, thereby improving diagnosis accuracy while reducing dependency on expert skill.
2Reliability
If multiple imaging techniques are used to improve detection accuracy, then the reliability of diagnosis improves, but the time consumption and cost increase
Solution Approach 1:
The patent creates a universal texture analysis system that can detect multiple types of anomalies (amyloid plaques, diabetic retinopathy, hypertensive retinopathy) using the same multi-wavelength imaging approach and GLCM-based texture analysis methodology. This single system replaces the need for multiple specialized imaging techniques, thereby maintaining high detection reliability across different diseases while reducing overall time consumption and cost.
Solution Approach 2:
The patent replaces complex mechanical and chemical imaging processes (such as PET scanning) with an optical-based texture analysis system using multi-wavelength photography and computational image processing. This substitution maintains diagnostic reliability for detecting tissue anomalies while significantly reducing time consumption and cost by using simpler, faster optical imaging techniques combined with automated GLCM analysis.
3Adaptability or versatility
If spectral information is added to images to improve anomaly detection, then the adaptability to detect various anomalies improves, but the device complexity increases
Solution Approach 1:
The patent adds spectral information as a third dimension to traditional two-dimensional images, creating multi-wavelength images that capture tissue properties across different spectral ranges. This dimensional enhancement improves adaptability to detect various anomalies (amyloid, diabetic, hypertensive conditions) while the complexity is managed through automated GLCM texture analysis that processes the spectral data systematically rather than requiring complex manual interpretation.
Data Source
AI summary
The present disclosure relates to a method and a system for imaging a biological tissue. A monochromatic image of the biological tissue is obtained. A texture analysis of the biological tissue is performed using spatial information of the monochromatic image to identity features of the biological tissue. A texture image is generated based on the features of the biological tissue. The biological tissue of the subject is classified as normal or abnormal at least in part based on a comparison between first order statistics of the texture image and predetermined values.