Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors

TL;DR

This paper evaluates different shape descriptors for classifying noisy cell contours, aiming to improve cell type identification and tissue analysis in biological and histopathological studies.

Contribution

It provides a comprehensive comparison of shape descriptors like Fourier, curvature, and dimensionality reduction methods for cell shape classification.

Findings

Elliptical Fourier Descriptors perform well on noisy contours.

Curvature features are effective for certain cell shapes.

Lower dimensional representations offer computational efficiency.

Abstract

This study addresses the challenge of classifying cell shapes from noisy contours, such as those obtained through cell instance segmentation of histological images. We assess the performance of various features for shape classification, including Elliptical Fourier Descriptors, curvature features, and lower dimensional representations. Using an annotated synthetic dataset of noisy contours, we identify the most suitable shape descriptors and apply them to a set of real images for qualitative analysis. Our aim is to provide a comprehensive evaluation of descriptors for classifying cell shapes, which can support cell type identification and tissue characterization-critical tasks in both biological research and histopathological assessments.