An efficient heuristic for geometric analysis of cell deformations

2025-01-26

Comput. Biol. Medicine

AI Summary

This study addresses the urgent need for efficient and accurate automated classification of red blood cells in sickle cell disease, where abnormal cell morphology impairs blood flow and oxygen delivery. The authors model red blood cells as closed planar curves in shape space and propose a novel framework for computing elastic shape distances based on principal axis alignment and fixed parameterization, circumventing the computationally expensive global reparameterization traditionally required. This approach significantly reduces computational complexity while preserving high accuracy. Integrating template matching, unsupervised clustering, and supervised classification, the method achieves 96.03% accuracy in both binary classification tasks, outperforming existing shape analysis techniques.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Dimensionality Reduction/Feature Selection
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Problem

• sickle cell disease
• erythrocyte classification
• cell deformation
• blood image analysis
• shape analysis

Innovation

• shape space
• elastic distance
• fixed parameterization
• template alignment
• sickle cell classification