Microparticle Classification Lookup Table Reduces Memory Usage
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Solution Overview
Problem
Existing methods for classifying microparticles using flow cytometry struggle to extend beyond two parameters due to exponential memory usage and complexity in graphical representation, making it difficult to handle systems with multiple parameters effectively.
Innovation Solution
The use of a lookup table framed by measurable parameters and umbrella values, which reduces data resolution to minimize memory usage and facilitate efficient classification by identifying locations within the table that correspond to predefined algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If bitmap-based classification is extended to more than two parameters, then classification capability is improved, but memory usage increases exponentially
Solution Approach 1:
The patent segments the classification space by creating a hierarchical structure where a small set of representative points (centroids) are selected from the parameter space. Each centroid represents a cluster of similar particles, and the space is divided into regions associated with each centroid. This segmentation allows multi-parameter classification without requiring a complete bitmap of all possible parameter combinations, thus reducing memory usage while maintaining classification capability.
Solution Approach 2:
The patent transforms the classification approach from a direct multi-dimensional bitmap representation to a reduced-dimensional representation using centroids and their associated parameters. Instead of storing classification information for every possible combination of parameter values across multiple dimensions, the system uses a smaller set of representative points (centroids) with associated weight vectors, effectively projecting the high-dimensional classification space into a lower-dimensional manageable structure.
2Measurement precision
If bitmap resolution is increased for each parameter, then measurement precision is improved, but memory usage increases linearly with resolution
Solution Approach 1:
The patent applies partial action by selecting only the most relevant parameters and their optimal weight combinations to define centroids, rather than creating bitmaps for all possible parameter resolutions. The system determines a subset of parameters and their weightings that sufficiently characterize the particle populations, avoiding the memory overhead of representing every possible parameter combination at high resolution while maintaining adequate measurement precision for classification purposes.
Data Source
AI summary
Methods, data structures, and systems for classifying particles are provided. In particular, the methods and systems are configured to acquire a first set of data corresponding to measurable parameters of a microparticle and identify a location of a look-up table to which the first set of data corresponds, wherein the look-up table is framed by values associated with at least one of the measurable parameters. Furthermore, the methods and systems are configured to determine whether the first set of data fits one or more predefined algorithms respectively indicative of a different microparticle classification associated with the identified location of the look-up table. The methods and systems are further configured to classifying the microparticle within at least one predefined categorization based upon the determination of whether the first set of data fits the one or more predefined algorithms.