3D Image Segmentation via Hessian Eigenvalue Texture Filtering
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Solution Overview
Problem
Current image analysis techniques, particularly for cellular imaging, face challenges in efficiently segmenting and classifying 3D images due to the complexity and heterogeneity of cellular samples, leading to inefficient processing and analysis of large datasets.
Innovation Solution
The use of computationally efficient systems and methods based on Hessian eigenvalues for texture filtering, which apply Gaussian second-derivative filters to create 3x3 Hessian matrices and compute eigenvalues for each voxel, allowing for the calculation of texture feature values and subsequent classification and segmentation of objects in 3D images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image analysis techniques are used for 3D cellular images, then processing can be performed with simple methods, but segmentation and classification accuracy is insufficient due to cellular heterogeneity
Solution Approach 1:
The patent divides the 3D image analysis into multiple processing stages: initial object identification, texture filtering application to identified objects, texture feature calculation, and final classification. This segmented approach allows complex analysis to be applied selectively to identified cellular objects rather than the entire image volume, improving segmentation accuracy while managing computational complexity through hierarchical processing.
Solution Approach 2:
The patent transitions from analyzing 3D volumetric data to extracting 2D texture feature matrices that characterize cellular objects. By computing Hessian eigenvalues and converting them into rotationally invariant texture features, the method projects 3D structural information into 2D feature space that can be efficiently classified, thereby improving segmentation accuracy without proportionally increasing computational burden.
2Measurement precision
If complex texture filtering based on Hessian eigenvalues is applied to each voxel, then classification and segmentation performance is improved, but computational processing time increases
Solution Approach 1:
The patent performs preliminary object identification in the 3D image before applying computationally intensive texture filtering. By first identifying candidate cellular objects through simpler methods, the system limits subsequent Hessian eigenvalue calculations to only the voxels within identified objects rather than the entire image volume. This preliminary action significantly reduces processing time while maintaining classification accuracy on the objects of interest.
Solution Approach 2:
The patent applies different levels of analysis to different regions: simple object identification to the entire 3D volume, then sophisticated texture filtering and feature extraction only to voxels within identified cellular objects. This local application of complex processing ensures high classification accuracy for cellular structures while avoiding unnecessary computation in background or non-cellular regions, thereby reducing overall processing time.
3Measurement precision
If rotationally invariant texture filters are used for heterogeneous cellular samples, then object classification accuracy is improved, but the complexity of processing heterogeneity increases
Solution Approach 1:
The patent transforms the Hessian eigenvalues (which are sensitive to rotation and orientation) into rotationally invariant texture features by applying mathematical transformations that eliminate directional dependence. This parameter change allows the classification system to accurately distinguish heterogeneous cellular objects regardless of their orientation in the 3D space, improving classification accuracy while the modular implementation keeps processing complexity manageable.
Solution Approach 2:
The patent develops a universal texture filtering framework based on Hessian eigenvalues that can process diverse cellular objects with varying shapes, sizes, and orientations using the same mathematical operations. The rotationally invariant texture filters provide a multi-functional solution that handles heterogeneous cellular samples uniformly, improving classification accuracy across different cell types while avoiding the need for separate processing pipelines for each object type.
Data Source
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AI summary
Described herein are computationally efficient systems and methods for processing and analyzing two-dimensional (2D) and three-dimensional (3D) images using texture filters that are based on the Hessian eigenvalues (e.g., eigenvalues of a square matrix of second-order partial derivatives) of each pixel or voxel. The original image may be a single image or a set of multiple images. In certain embodiments, the filtered images are used to calculate texture feature values for objects such as cells identified in the image. Once objects are identified, the filtered images can be used to classify the objects, for image segmentation, and/or to quantify the objects (e.g., via regression).