Optical Skin Image Processing for Speckle-Reduced Melanin Segmentation
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Solution Overview
Problem
Existing non-invasive imaging techniques for skin pigmentation, such as FF-OCT, struggle with speckle noise and lack quantitative analysis of melanin distribution, limiting effective diagnosis and treatment of pigment disorders.
Innovation Solution
A method involving spatial compounding-based denoising convolutional neural networks (SC-DnCNN) for noise reduction, followed by contrast enhancement and object segmentation, enabling precise quantification of melanin features in optical images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If non-invasive imaging techniques (FF-OCT, RCM, multiphoton microscopy) are used to detect cellular changes in skin, then patient comfort and safety are improved, but image quality and quantitative analysis capability deteriorate due to speckle noise
Solution Approach 1:
The patent segments melanin features from optical images through a multi-step process: noise reduction to separate signal from speckle noise, contrast enhancement to distinguish melanin from background, and threshold-based segmentation to isolate melanin regions. This enables quantitative analysis of melanin distribution while maintaining non-invasive imaging benefits.
Solution Approach 2:
The patent introduces image processing algorithms as intermediaries between the optical imaging system and quantitative analysis. The processing pipeline (noise reduction → contrast enhancement → segmentation → quantification) acts as a mediator that transforms noisy optical images into reliable quantitative melanin distribution data.
2Measurement precision
If traditional histopathological sections are used to visualize cellular changes, then diagnostic accuracy is improved, but patient comfort and invasiveness deteriorate
Solution Approach 1:
The patent creates a digital copy of histopathological analysis through non-invasive optical imaging. By processing optical images to extract and quantify melanin distribution, the system replicates the diagnostic information previously obtainable only through invasive tissue sectioning, eliminating the need for physical biopsies.
3Loss of time
If optical images are processed without noise reduction, then processing time is reduced, but image quality and feature detection capability deteriorate due to speckle noise
Solution Approach 1:
The patent applies noise reduction as a preliminary step before contrast enhancement and segmentation. By removing speckle noise early in the processing pipeline, subsequent steps can operate more efficiently on cleaner data, achieving both improved melanin detection accuracy and reasonable processing times.
Data Source
AI summary
Provided herein is a method of segmenting features from an optical image of a skin comprising steps of receiving an optical image of a skin that contains at least one feature of an object: contrast-enhancing the feature's signals of the optical image from the background signals: segmenting the object in the enhanced optical image, and quantifying the feature from the optical image of the skin.