Threshold Compactness Features for Cell Phenotype Classification
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Solution Overview
Problem
Existing automated image processing techniques are inefficient and inaccurate in distinguishing and classifying different cell phenotypes in digital images, particularly in identifying sub-cellular objects and distinguishing them from image artifacts.
Innovation Solution
The introduction of threshold compactness features, which involve applying thresholds and binary masks to images to identify provisional objects, calculating their compactness based on area and border length, and using these features for robust and efficient classification of cell phenotype.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing automated image processing techniques are used to identify and classify cell phenotypes, then the classification process can be standardized and automated, but the techniques are incapable of distinguishing among different cell phenotypes and are overly complicated and computationally expensive
Solution Approach 1:
The patent introduces a new morphological parameter called 'threshold compactness' that combines thresholding with compactness calculation. This parameter change enables simple yet effective distinction between different cell phenotypes, avoiding the need for complex existing techniques while maintaining automation.
Solution Approach 2:
The patent replaces complex mechanical/image processing systems with a simplified approach based on threshold compactness features. By substituting the complex processing pipeline with a focused measurement of threshold compactness, the system achieves the same classification capability with reduced complexity and computational cost.
2Extent of automation
If existing image processing techniques are used to identify sub-cellular objects, then automated classification can be performed, but the techniques cannot reliably distinguish sub-cellular objects from image artifacts
Solution Approach 1:
The patent introduces threshold compactness as a new morphological parameter that provides reliable discrimination between genuine sub-cellular objects and image artifacts. This parameter change enables automated detection to become reliable by capturing essential morphological differences that existing techniques miss.
3Extent of automation
If existing image processing techniques are used for cell phenotype classification, then standardization is achieved, but the techniques are computationally expensive and inefficient
Solution Approach 1:
The patent extracts and focuses on a single key feature - threshold compactness - from the complex suite of morphological measurements. By taking out only the most discriminative feature, the system maintains standardized classification while dramatically reducing computational requirements and improving efficiency.
Solution Approach 2:
The introduction of threshold compactness as a new parameter enables efficient computation while maintaining classification accuracy. This parameter change allows the system to achieve standardized classification with significantly reduced computational cost compared to existing techniques.
Data Source
AI summary
A new family of morphological features, referred to herein as threshold compactness features, is provided, useful for automated classification of objects, such as cells, in images. In one embodiment, one or more thresholds and/or binary masks are applied to an image, and one or more provisional objects within a cell in the image are automatically identified. The threshold compactness of the cell is computed as a function of area S of the one or more provisional objects and border length P of the one or more provisional objects. Computation of threshold compactness allows cells in an image to be distinguished and characterized. Compared to previous techniques, the methods and apparatus described herein are more robust and computationally efficient.