IEOPF: An Active Contour Model for Image Segmentation with Inhomogeneities Estimated by Orthogonal Primary Functions

TL;DR

This paper introduces IEOPF, a novel active contour model that effectively segments images with intensity inhomogeneities by estimating bias fields using orthogonal primary functions, improving accuracy over existing methods.

Contribution

The paper proposes a new level set model that incorporates orthogonal primary functions for bias estimation, extending to multichannel and multiphase images for improved segmentation.

Findings

Outperforms state-of-the-art methods in bias correction and segmentation accuracy.

Effective on synthetic and real datasets including BrainWeb and IBSR.

Applicable to colorful and multi-object images.

Abstract

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.