Trainable segmentation for transmission electron microscope images of inorganic nanoparticles.

ABSTRACT: We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and

SUBMITTER: Bell CG 

PROVIDER: S-EPMC10084002 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature