{"title":"Contour Analysis Tool: an interactive tool for background and morphology analysis","authors":"Mark A. Hutchison, Christine M. Koepferl","doi":"arxiv-2409.06421","DOIUrl":null,"url":null,"abstract":"We introduce the Contour Analysis Tool (CAT), a Python toolkit aimed at\nidentifying and analyzing structural elements in density maps. CAT employs\nvarious contouring techniques, including the lowest-closed contour (LCC),\nlinear and logarithmic Otsu thresholding, and average gradient thresholding.\nThese contours can aid in foreground and background segmentation, providing\nnatural limits for both, as well as edge detection and structure\nidentification. Additionally, CAT provides image processing methods such as\nsmoothing, background removal, and image masking. The toolkit features an\ninteractive suite of controls designed for Jupyter environments, enabling users\nto promptly visualize the effects of different methods and parameters. We\ndescribe, test, and demonstrate the performance of CAT, highlighting its\npotential use cases. CAT is publicly available on GitHub, promoting\naccessibility and collaboration.","PeriodicalId":501068,"journal":{"name":"arXiv - PHYS - Solar and Stellar Astrophysics","volume":"181 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Solar and Stellar Astrophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06421","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We introduce the Contour Analysis Tool (CAT), a Python toolkit aimed at
identifying and analyzing structural elements in density maps. CAT employs
various contouring techniques, including the lowest-closed contour (LCC),
linear and logarithmic Otsu thresholding, and average gradient thresholding.
These contours can aid in foreground and background segmentation, providing
natural limits for both, as well as edge detection and structure
identification. Additionally, CAT provides image processing methods such as
smoothing, background removal, and image masking. The toolkit features an
interactive suite of controls designed for Jupyter environments, enabling users
to promptly visualize the effects of different methods and parameters. We
describe, test, and demonstrate the performance of CAT, highlighting its
potential use cases. CAT is publicly available on GitHub, promoting
accessibility and collaboration.