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.

翻译：摘要：医学研究人员通过物理测量和图像数据获取关于残疾与疾病预防和治疗的知识。为协助这一工作，现有特征提取工具包旨在从图像结构中收集数据。在本研究中，我们旨在通过增加当前基于形状的特征种类来扩展现有工作。基于形状的特征的重要性在数十年研究中已被广泛探讨，但目前尚无单一工具包能让研究人员轻松提取所有形状相关特征。PyCellMech正是为填补这一空白而设计。该工具包提取三类形状特征，分别归类为一维特征、几何特征和多边形特征。未来版本将扩展至包含其他特征类别，如尺度空间特征。可用性与实现：PyCellMech可在 https://github.com/icm-dac/pycellmech 免费获取。