Open AccessA hybrid level set model for image segmentation
Author(s) -
Weiqin Chen,
Changjiang Liu,
Anup Basu,
Bin Pan
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0251914
Subject(s) - active contour model , maxima and minima , artificial intelligence , level set (data structures) , image segmentation , computer science , segmentation , smoothing , computer vision , position (finance) , pattern recognition (psychology) , level set method , process (computing) , image (mathematics) , enhanced data rates for gsm evolution , energy (signal processing) , iterative and incremental development , edge detection , image processing , mathematics , mathematical analysis , statistics , software engineering , finance , economics , operating system
Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information. The local information of the model is obtained by bilateral filters, which can also enhance the edge information while smoothing the image. The local fitting centers are calculated before the contour evolution, which can alleviate the iterative process and achieve fast image segmentation. The global information of the model is obtained by simplifying the C-V model, which can assist contour evolution, thereby increasing accuracy. Experimental results show that our algorithm is insensitive to the initial contour position, and has higher precision and speed.