Medical Image Volume Rendering With Segmented Intensity Offsets
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Solution Overview
Problem
Existing volume rendering methods for medical images fail to accurately represent anatomical structures with overlapping intensity values, leading to occlusion issues and lack of spatial distinction in appearance.
Innovation Solution
A method involving segmentation of voxel grids to assign labels to regions of interest, adding offset values based on labels, and applying piecewise transfer functions to enhance the visibility and distinction of anatomical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional volume rendering with single transfer function is used, then visualization is simple and fast, but anatomical structures with overlapping intensity values cannot be distinguished
Solution Approach 1:
The patent segments the volume data into multiple labeled regions corresponding to different anatomical structures. Each segment is assigned a unique label that identifies the anatomical structure, enabling distinction even when intensity values overlap. This segmentation approach allows the system to apply different transfer functions to different anatomical structures, resolving the contradiction between visualization simplicity and structural distinction.
Solution Approach 2:
The patent applies different transfer functions to different spatial regions corresponding to different anatomical structures. Each anatomical structure receives a customized transfer function that optimizes its visual representation. This local quality approach enables precise control over the appearance of each anatomical structure while maintaining overall visualization coherence, thus improving distinction without excessive complexity.
2Measurement precision
If surface representation is used for selected objects, then spatial distinction is improved, but granularity is lost and not all anatomical structures are suitable
Solution Approach 1:
The patent creates a universal volume rendering system that can handle all types of anatomical structures through the combination of segmentation labels and multiple transfer functions. The system maintains the advantages of volume rendering (granularity, suitability for all structures) while incorporating spatial awareness through labels. This multi-functional approach allows the same system to effectively represent diverse anatomical structures including those with fine details like thin blood vessels, eliminating the need to switch between surface and volume representations.
3Measurement precision
If multiple transfer functions are applied to different segments, then anatomical structures gain distinctive appearances, but computational complexity increases
Solution Approach 1:
The patent performs segmentation and assigns labels to volumetric data before the rendering process. This preliminary action organizes the data into distinct anatomical structures with unique identifiers, allowing efficient application of multiple transfer functions during rendering. By pre-segmenting the volume and storing label information, the system avoids computationally expensive real-time segmentation, thus reducing overall computational complexity while maintaining appearance distinction.
Data Source
AI summary
A computer-implemented method for volume rendering a set of medical images of a patient. The method comprises obtaining the set of medical images defining a voxel grid comprising voxels. The voxels are each associated to a value (hereinafter also referred to as “intensity value”). The method comprises segmenting the voxel grid defined by the set of medical images by associating to each voxel a respective label among a set of labels each corresponding to a respective region of interest of the patient. The method comprises modifying values of the voxel grid by adding to each value to be modified an offset value which depends on the respective label associated to the voxel. Such a method provides an improved solution for volume rendering a set of medical images of a patient.