PyCellMech: A shape-based feature extraction pipeline for use in medical and biological studies
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911886203682816 |
|---|---|
| author | Arslan, Janan Chhoa, Henri Khemir, Ines Valabregue, Romain Benke, Kurt K. |
| author_facet | Arslan, Janan Chhoa, Henri Khemir, Ines Valabregue, Romain Benke, Kurt K. |
| contents | Summary: Medical researchers obtain knowledge about the prevention and treatment of disability and disease using physical measurements and image data. To assist in this endeavor, feature extraction packages are available that are designed to collect data from the image structure. In this study, we aim to augment current works by adding to the current mix of shape-based features. The significance of shape-based features has been explored extensively in research for several decades, but there is no single package available in which all shape-related features can be extracted easily by the researcher. PyCellMech has been crafted to address this gap. The PyCellMech package extracts three classes of shape features, which are classified as one-dimensional, geometric, and polygonal. Future iterations will be expanded to include other feature classes, such as scale-space.
Availability and implementation: PyCellMech is freely available at https://github.com/icm-dac/pycellmech. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15567 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyCellMech: A shape-based feature extraction pipeline for use in medical and biological studies Arslan, Janan Chhoa, Henri Khemir, Ines Valabregue, Romain Benke, Kurt K. Computer Vision and Pattern Recognition Summary: Medical researchers obtain knowledge about the prevention and treatment of disability and disease using physical measurements and image data. To assist in this endeavor, feature extraction packages are available that are designed to collect data from the image structure. In this study, we aim to augment current works by adding to the current mix of shape-based features. The significance of shape-based features has been explored extensively in research for several decades, but there is no single package available in which all shape-related features can be extracted easily by the researcher. PyCellMech has been crafted to address this gap. The PyCellMech package extracts three classes of shape features, which are classified as one-dimensional, geometric, and polygonal. Future iterations will be expanded to include other feature classes, such as scale-space. Availability and implementation: PyCellMech is freely available at https://github.com/icm-dac/pycellmech. |
| title | PyCellMech: A shape-based feature extraction pipeline for use in medical and biological studies |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.15567 |