Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912100182392832 |
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| author | Vadori, Valentina Peruffo, Antonella Graïc, Jean-Marie Finos, Livio Grisan, Enrico |
| author_facet | Vadori, Valentina Peruffo, Antonella Graïc, Jean-Marie Finos, Livio Grisan, Enrico |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_00561 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors Vadori, Valentina Peruffo, Antonella Graïc, Jean-Marie Finos, Livio Grisan, Enrico Computer Vision and Pattern Recognition Quantitative Methods 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. |
| title | Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors |
| topic | Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2411.00561 |