比较多种形状描述符，找出最适合噪声细胞轮廓分类的方法。
Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors
- 用合成噪声数据测试椭圆傅里叶、曲率等形状特征
- 发现特定描述符在真实组织图像上表现更优
- 适合生物研究与病理诊断中的细胞类型识别
本研究针对从组织学图像中通过细胞实例分割获得的噪声轮廓，解决细胞形状分类难题。我们评估了多种形状特征的表现，包括椭圆傅里叶描述符（Elliptical Fourier Descriptors）、曲率特征以及低维表示。基于一个标注的合成噪声轮廓数据集，我们筛选出最合适的形状描述符，并将其应用于真实图像进行定性分析。目标是为细胞形状分类提供全面的描述符评估，支持细胞类型识别与组织表征——这对生物研究和病理学评估至关重要。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。