Profile Weighted Intensity Features for Cell Phenotype Classification
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Solution Overview
Problem
Existing automated image processing techniques are inefficient and inaccurate in distinguishing different cell phenotypes in digital images, lacking effective morphological features for accurate classification.
Innovation Solution
The introduction of profile weighted intensity features, which emphasize pixel intensities near cell borders and normalize them to reduce variations, combined with a sliding parabola erosion operation for efficient computation, enables more accurate and efficient cell phenotype classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing automated image processing techniques are used for cell classification, then the classification process can be standardized, but the techniques are incapable of distinguishing among different cell phenotypes
Solution Approach 1:
The patent transforms the image data by computing pixel intensities as a function of distance from cell borders, creating a new parameter space that emphasizes morphological features. This parameter transformation enables the extraction of distance-based intensity features that are discriminatory for cell phenotype classification while maintaining computational feasibility through efficient algorithms
2Productivity
If existing image processing techniques are used, then automation is achieved, but the techniques are overly complicated and computationally intensive
Solution Approach 1:
The patent segments the cell image into concentric distance-based zones from the border, computing features for each zone separately. This segmentation approach simplifies the computational problem by breaking down the complex image analysis into manageable distance-based regions, improving both speed and efficiency
Solution Approach 2:
The patent extracts only the relevant morphological features by computing pixel intensities as a function of distance from cell borders, discarding irrelevant information. This selective extraction of distance-based intensity features reduces computational complexity while maintaining classification accuracy
3Measurement precision
If absolute intensities are used for cell classification, then the full intensity information is available, but large cell-to-cell variations reduce classification accuracy
Solution Approach 1:
The patent applies different weighting to different spatial regions of the cell by computing pixel intensities as a function of distance from the border. This local quality approach emphasizes border regions while de-emphasizing central regions, creating features that are more robust to cell-to-cell variations and improve classification accuracy
Data Source
AI summary
A new morphological feature referred to herein as profile weighted intensity feature is provided, useful for automated classification of objects, such as cells, that are depicted in digital images. In certain embodiments, the profile weighted intensity feature is determined by automatically identifying a border of a cell in an input image, determining a distance image for the cell, computing a profile function for the cell, and computing a mean intensity of at least a portion of the input image weighted by the profile function for the cell.