LMCC Image Characterization for Dense Tissue Lesion Detection
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Solution Overview
Problem
Existing image processing and analysis methodologies, particularly in medical imaging, struggle to effectively visualize and characterize objects within images due to high false positive and false negative rates, especially in dense breast tissue, leading to challenges in distinguishing cancerous lesions from surrounding tissues.
Innovation Solution
The use of a convergence-based system, such as local micro-contrast convergence (LMCC) algorithms, which iteratively transform digital images to uniquely express tissue structures, allowing for the visualization and characterization of objects by causing all tissues to converge into characteristic patterns, reducing false positives and negatives through multi-dimensional image transformations and look-up tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image processing methodologies are used, then the processing is simple and fast, but the false positive and false negative rates are high and tissue characterization is poor
Solution Approach 1:
The image processing is divided into multiple sequential stages: initial image acquisition, multi-dimensional color space transformation, iterative local micro-contrast convergence processing, and final visualization. Each stage processes specific features independently, allowing complex characterization while maintaining manageable computational steps
Solution Approach 2:
The patent transforms images from standard grayscale or RGB spaces into multi-dimensional color spaces with additional channels representing different tissue optical properties. This dimensional expansion enables differentiation of tissue types that appear similar in conventional imaging, reducing false positives and negatives
2Measurement precision
If multi-dimensional image transformations are applied, then tissue characterization improves, but processing time and computational resources increase
Solution Approach 1:
The local micro-contrast convergence is implemented as an iterative periodic process that repeatedly applies transformation functions until convergence criteria are met. This allows progressive refinement of tissue characterization without requiring all computations to be performed simultaneously, optimizing resource usage
Solution Approach 2:
The patent dynamically adjusts transformation parameters and convergence thresholds based on image characteristics and processing progress. By adapting parameters during processing rather than using fixed values, the system achieves high precision while minimizing unnecessary computational iterations
Data Source
AI summary
A method of visualization, characterization, and detection of objects within an image by applying a local micro-contrast convergence algorithm to a first image to produce a second image that is different from the first image, wherein all like objects converge into similar patterns or colors in the second image.