PyCellMech: A shape-based feature extraction pipeline for use in medical and biological studies
This provides a tool for medical and biological researchers to easily extract shape features, but it is incremental as it adds to existing feature extraction methods.
The authors tackled the lack of a comprehensive package for extracting shape-based features from medical and biological images by developing PyCellMech, which extracts three classes of shape features (one-dimensional, geometric, and polygonal) and is freely available for use.
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.