Skin Pigmentation Image Segmentation for Vitiligo Quantification
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Solution Overview
Problem
Current methods for diagnosing and monitoring vitiligo are subjective, invasive, time-consuming, and require expensive equipment, limiting their practicality and precision.
Innovation Solution
A system and method utilizing feature selection methods, machine learning algorithms, and image processing techniques to automate the diagnosis and quantification of vitiligo, employing color spaces like HSV, LAB, and RGB, and a clustering algorithm for accurate segmentation of affected skin regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual examination by dermatologist is used, then diagnosis can be performed, but the subjective nature leads to inconsistencies and lacks precision
Solution Approach 1:
The patent replaces the manual visual examination system with an automated image processing and machine learning system. The controller processes images through multiple color models (RGB, HSV, LAB) and applies clustering algorithms to objectively quantify vitiligo affected area, eliminating subjectivity while maintaining simplicity in operation.
2Measurement precision
If Wood's lamp examination is used, then contrast between vitiliginous and normal skin is enhanced, but specific lighting conditions and expert interpretation are required
Solution Approach 1:
The patent uses standard digital imaging to capture skin images without requiring special Wood's lamp equipment. The image processing system digitally enhances contrast through color model transformations and clustering algorithms, replicating the enhanced visualization effect while eliminating the need for specialized lighting conditions and expert interpretation skills.
3Measurement precision
If skin biopsy is used, then definitive diagnosis is obtained, but the procedure is invasive and not practical for monitoring
Solution Approach 1:
The patent replaces the invasive mechanical biopsy procedure with a non-invasive optical imaging and computational analysis system. The machine learning algorithm analyzes color variations in digital images to provide definitive diagnosis and ongoing monitoring without causing any harm or requiring tissue extraction.
4Measurement precision
If existing imaging techniques with computer-aided analysis are used, then objective quantification is attempted, but the methods lack precision or require expensive equipment
Solution Approach 1:
The patent creates a universal image processing system that works with standard digital images without requiring specialized expensive equipment. The controller implements multiple color models (RGB, HSV, LAB) and clustering algorithms that can be applied to any standard skin image, providing precise quantification of vitiligo affected area across different devices and settings.
Data Source
AI summary
The present disclosure relates to systems and methods for quantifying hypo- or hyper-skin pigmentation conditions. An example method includes providing an image of a skin surface. The method also includes selecting a plurality of color channels from among a plurality of color models. The method yet also includes forming a color-adjusted version of the image based on the selected combination of color channels. The method additionally includes extracting a mask based on the color-adjusted version of the image. The method yet further includes determining, based on the extracted mask, a normal portion of the skin surface. The method also includes determining, based on the extracted mask, a differently-pigmented portion of the skin surface. The method additionally includes providing information indicative of the differently-pigmented portion of the skin surface.