Automated Cell Classification Using Matrix Feature Extraction
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Solution Overview
Problem
Manual inspection and classification of biological samples are time-consuming and prone to errors due to subjective human judgment, necessitating the development of automated systems for accurate classification of disease states in cells.
Innovation Solution
An automated method that determines the positions of cells from images, generates matrices with two-dimensional information about neighboring cells, and classifies cells into multiple classes based on numerical features derived from these matrices, including one-dimensional and two-dimensional distributions, and pixel texture information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection and classification of biological samples is performed, then classification can be done with human judgment, but it is time-consuming and prone to errors
Solution Approach 1:
The patent replaces the mechanical human inspection system with an automated computational system that uses image processing algorithms to detect, extract features, and classify cells. This substitution eliminates human subjectivity and time constraints while maintaining or improving classification accuracy through consistent application of mathematical algorithms.
Solution Approach 2:
The system enables self-service classification by automatically performing all steps from image acquisition to final classification without human intervention. The automated extraction of nuclear position information and pixel texture features, combined with machine learning classifiers, allows the system to independently complete the classification task that previously required human pathologists.
2Productivity
If automated classification systems are used to examine biological samples, then time consumption is reduced and efficiency is increased, but classification accuracy must be maintained without human judgment
Solution Approach 1:
The patent enhances classification accuracy by moving from traditional single-feature analysis to multi-dimensional feature space. It simultaneously extracts and analyzes nuclear position information (spatial dimension) and pixel texture information (intensity dimension), creating a comprehensive feature vector that provides richer information for classification while maintaining high efficiency through automated processing.
Solution Approach 2:
The system combines multiple types of features (nuclear position features and pixel texture features) into a composite feature representation, analogous to composite materials in engineering. This combination leverages the complementary strengths of different feature types to achieve superior classification performance compared to using individual feature types alone.
3Device complexity
If only nuclear position information is used for classification, then the process is simpler, but classification accuracy is limited
Solution Approach 1:
The patent merges nuclear position information and pixel texture information into a unified classification framework. By combining these two complementary feature types, the system achieves enhanced classification accuracy that surpasses what either feature type can provide individually, while the integrated approach manages complexity through systematic feature extraction and combination.
Data Source
AI summary
Methods disclosed herein include: (a) determining positions of a plurality of cells based on one or more images of the cells; (b) for at least some of the plurality of cells, generating a matrix that includes two-dimensional information about positions of neighboring cells, and determining one or more numerical features based on the information in the matrix; and (c) classifying the at least some of the plurality of cells as belonging to at least one of multiple classes based on the numerical features.