3D Mesh Surface Deviation Visualization via Color Blending
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Solution Overview
Problem
Current medical imaging technologies face challenges in converting hundreds of sectional images into effective three-dimensional representations due to conversion methodologies, surface color manipulation, and insufficient image resolution, hindering qualitative and quantitative analyses.
Innovation Solution
A computer-implemented method and system for transforming multiple datasets into three-dimensional mesh surface visualization, allowing for qualitative and quantitative comparisons by blending primary colors to create secondary colors and computing distance differentials between mesh surface structures, enabling visualization and analysis of deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If hundreds of sectional images are converted into three-dimensional representations, then the visualization capability is improved, but the conversion complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex conversion process into distinct modules: a surface extraction module that processes sectional images to generate initial surface data, a mesh generation module that creates mesh structures from the surface data, and a color mapping module that applies color coding. This segmentation allows each module to handle specific tasks independently, reducing overall conversion complexity while maintaining visualization quality.
Solution Approach 2:
The patent transitions from two-dimensional sectional images to three-dimensional mesh surface representations by introducing a third spatial dimension. This dimensional transformation enables comprehensive visualization of anatomical structures, allowing medical professionals to examine surfaces from multiple angles and assess deviations in three-dimensional space rather than limited two-dimensional slices.
2Difficulty of detecting and measuring
If surface color manipulation is used to highlight deviations, then the detection capability is improved, but the color accuracy and quantification precision may be compromised
Solution Approach 1:
The patent implements a color mapping module that assigns color codes to mesh surface elements based on quantified deviation values. The system uses a standardized color scale where different colors represent specific ranges of deviation measurements, enabling both qualitative visual detection and quantitative analysis. This approach maintains color accuracy by establishing direct, reproducible relationships between measured deviations and displayed colors.
Solution Approach 2:
The patent introduces a deviation calculation module as an intermediary between the mesh generation and color mapping processes. This module objectively computes deviation values by comparing the target surface geometry with reference geometry, providing precise numerical data that serves as an accurate intermediary for subsequent color coding. This intermediary step ensures that color representation is based on rigorous quantitative measurement rather than subjective visualization.
3Measurement precision
If image resolution is increased to improve analysis quality, then the measurement precision is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary surface extraction and mesh generation from the sectional images before conducting detailed deviation analysis. By pre-processing the data into optimized mesh structures with appropriate resolution levels, the system prepares the geometry in advance for efficient comparison operations. This preliminary action reduces the computational burden during the actual deviation calculation phase, decreasing processing time while maintaining measurement precision.
Solution Approach 2:
The patent applies different resolution levels to different regions of the anatomical structure based on their importance and deviation characteristics. Areas with significant deviations or clinical relevance receive higher mesh density and finer resolution, while regions with minimal variation use coarser sampling. This localized quality adjustment optimizes the balance between measurement precision in critical areas and overall processing efficiency.
Data Source
AI summary
Embodiments of the present disclosure are directed to methods and computer systems for converting datasets into three-dimensional (“3D”) mesh surface visualization, displaying the mesh surface on a computer display, comparing two three-dimensional mesh surface structures by blending two primary different primary colors to create a secondary color, and computing the distance between two three-dimensional mesh surface structures converted from two closely-matched datasets. For qualitative analysis, the system includes a three-dimensional structure comparison control engine that is configured to convert dataset with three-dimensional structure into three-dimensional surfaces with mesh surface visualization. The control engine is also configured to assign color and translucency value to the three-dimensional surface for the user to do qualitative comparison analysis. For quantitative analysis, the control engine is configured to compute the distance field between two closely-matched datasets.