1 research outputs found
IEOPF: An Active Contour Model for Image Segmentation with Inhomogeneities Estimated by Orthogonal Primary Functions
Image segmentation is still an open problem especially when intensities of
the interested objects are overlapped due to the presence of intensity
inhomogeneity (also known as bias field). To segment images with intensity
inhomogeneities, a bias correction embedded level set model is proposed where
Inhomogeneities are Estimated by Orthogonal Primary Functions (IEOPF). In the
proposed model, the smoothly varying bias is estimated by a linear combination
of a given set of orthogonal primary functions. An inhomogeneous intensity
clustering energy is then defined and membership functions of the clusters
described by the level set function are introduced to rewrite the energy as a
data term of the proposed model. Similar to popular level set methods, a
regularization term and an arc length term are also included to regularize and
smooth the level set function, respectively. The proposed model is then
extended to multichannel and multiphase patterns to segment colourful images
and images with multiple objects, respectively. It has been extensively tested
on both synthetic and real images that are widely used in the literature and
public BrainWeb and IBSR datasets. Experimental results and comparison with
state-of-the-art methods demonstrate that advantages of the proposed model in
terms of bias correction and segmentation accuracy.Comment: 27 pages, 14 figure