Open AccessConvex‐relaxed active contour model based on localised kernel mapping
Author(s) -
Cui Wenchao,
Gong Guoqiang,
Lu Ke,
Sun Shuifa,
Dong Fangmin
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0132
Subject(s) - segmentation , energy functional , active contour model , pixel , computer science , artificial intelligence , image segmentation , regular polygon , kernel (algebra) , computer vision , intensity mapping , intensity (physics) , mathematics , pattern recognition (psychology) , mathematical optimization , algorithm , optics , geometry , physics , mathematical analysis , redshift , combinatorics , quantum mechanics , galaxy
Intensity inhomogeneity is one of the major obstacles for intensity‐based segmentation in many applications. The recently proposed kernel mapping (KM) method has exhibited excellent performance on segmenting various types of noisy images while it is not effective to handle intensity inhomogeneity. To overcome this drawback, this study presents a localised KM (LKM) method based on the fact that intensity inhomogeneity can be ignored in a local neighbourhood. The authors’ method first reconstructs the KM formulation of image segmentation in a neighbourhood of each pixel, and then such formulations for all pixels can be integrated together to derive the LKM energy functional. Minimisation of the energy functional is implemented by solving an equivalent convex‐relaxed problem whose optimisation can be quickly achieved via the split Bregman method. Experimental results on two‐phase segmentation and multiphase segmentation demonstrate competitive performance of the LKM method in the presence of intensity inhomogeneity and severe noise.