Point Density Illustration for Anatomical Mapping
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Solution Overview
Problem
In anatomical mapping procedures, such as those involving the heart, it is challenging for medical professionals to differentiate and identify sparse regions within dense areas of map points, as current methods do not effectively distinguish between points in dense and sparse regions, leading to difficulties in visualizing and manipulating the map accurately.
Innovation Solution
A method and system that compute the density of points within a region surrounding each point on a surface, rendering points with sizes inversely related to their density, allowing for larger points in sparse regions and smaller points in dense regions, thereby emphasizing sparse areas and aiding in their identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform point sizes are used in the map display, then the display is simple and consistent, but sparse regions cannot be differentiated from dense regions
Solution Approach 1:
The patent applies local quality by varying the size of display elements (points) based on their local density context. Points in sparse regions are rendered larger than points in dense regions, creating local visual differentiation that helps operators identify sparse areas without requiring complex additional processing or display mechanisms.
2Loss of information
If all points are rendered with the same size, then rendering is computationally simple, but visual differentiation of regions is lost
Solution Approach 1:
The patent segments the display area into multiple regions and calculates point density independently for each region. This segmentation approach allows the system to process and render points in manageable chunks, reducing overall computational burden while still achieving effective visual differentiation of sparse versus dense regions across the entire map.
Solution Approach 2:
The patent changes the size parameter of display elements based on local point density. By dynamically adjusting point sizes according to density calculations, the system recovers visibility information for sparse regions without requiring complex rendering algorithms, achieving effective differentiation through a single parameter modification.
3Loss of information
If point sizes vary based on density, then sparse regions become visible, but the rendering complexity increases
Solution Approach 1:
The rendering process implements local quality by applying different point sizes to different regions based on their density characteristics. This approach makes sparse areas detectable through simple visual inspection of point size variations, without requiring complex rendering pipelines or additional processing stages.
Data Source
AI summary
Methods, computing systems and computer software products implement embodiments of the present invention that include receiving, by a processor, respective coordinates for multiple points on a surface, and rendering an image of the surface on a display screen. For each given point among the multiple points, a density of the points within a region surrounding the given point is computed, and the given point on the surface with a point size that is inversely related to the computed density is rendered on the display screen.