Open AccessFBSLS model for image segmentation
Author(s) -
Su Ting,
She Lihuang,
Zhang Shi
Publication year - 2018
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.2017.0247
Subject(s) - segmentation , smoothing , computer science , algorithm , kernel (algebra) , image segmentation , function (biology) , basis function , pattern recognition (psychology) , artificial intelligence , mathematics , mathematical optimization , computer vision , mathematical analysis , combinatorics , evolutionary biology , biology
To achieve quick and accurate segmentations for intensity inhomogeneous images, the model named fractional B‐spline level set (FBSLS) is proposed in this study. The core of FBSLS method is that the LS function is expressed as the line combination of FBS basis in continuous forms. In this expression, the evolution of the energy function can be seen as a variation problem on the space spanned by the FBSs. As a result, the minimum value of energy function can be obtained directly from the coefficient of FBSs. Furthermore, every minimisation step can be considered as a convolution operation, and the evolution of the process of energy function may be regarded as the filtering operation whose kernel function is defined by the fractional order BS function. The filtering operation induces an intrinsic algorithm of smoothing which may be controlled explicitly by the order of the chosen FBS function. At the last, the morphological operator was used as the step of post‐processing to get better segmentation. The obtained experimental results demonstrate that the proposed model has better segment results than the traditional ones on segmentation time, Dice coefficient, precision, and recall and F‐measure metrics.