IMPPY3D: Image Processing in Python for 3D Image Stacks

Area of Science:

• Scientific Computing
• Image Analysis
• Materials Science

Background:

• 3D image analysis is crucial for characterizing complex structures in various scientific fields.
• Existing tools may lack comprehensive features for post-processing and shape characterization of volumetric data.
• Open-source solutions are valuable for accessibility and community-driven development in scientific software.

Purpose of the Study:

• To introduce Image Processing in Python for 3D image stacks (IMPPY3D), a novel FOSS repository.
• To provide a versatile tool for post-processing and 3D shape characterization of grayscale image stacks.
• To facilitate advanced analysis of volumetric data across diverse research applications.

Main Methods:

• IMPPY3D integrates original Python scripts, Cython extensions, and wrappers for libraries like SciKit-Image, OpenCV, and PyVista.
• It offers interactive parameter adjustment for image processing tasks.
• Key functionalities include segmentation, 3D filtering, object labeling, precise rotation, bounding box generation, and model conversion (voxel to mesh).

Main Results:

• IMPPY3D enables interactive manipulation and filtering of 2D and 3D images.
• The software facilitates segmentation and characterization of 3D shapes within volumetric data.
• It supports conversion of image stacks into 3D models (e.g., VTK files) and smooth mesh representations.

Conclusions:

• IMPPY3D provides a powerful and flexible platform for 3D image post-processing and shape analysis.
• Its capabilities are applicable to a wide range of research areas, including powder particle analysis and defect characterization in additive manufacturing.
• The open-source nature of IMPPY3D promotes wider adoption and further development in scientific image analysis.