Region Based Segmentation in Presence of Intensity Inhomogeneity Using Legendre Polynomials


We propose a novel region based segmentation method capable of segmenting objects in presence of significant intensity variation. Current solutions use some form of local processing to tackle intra-region inhomogeneity, which makes such methods susceptible to local minima. In this letter, we present a framework which generalizes the traditional Chan-Vese algorithm. In contrast to existing local techniques, we represent the illumination of the regions of interest in a lower dimensional subspace using a set of pre-specified basis functions. This representation enables us to accommodate heterogeneous objects, even in presence of noise. We compare our results with three state of the art techniques on a dataset focusing on biological/biomedical images with tubular or filamentous structures. Quantitatively, we achieve a 44% increase in performance, which demonstrates efficacy of the method.

DOI: 10.1109/LSP.2014.2346538

Extracted Key Phrases

3 Figures and Tables

Citations per Year

66 Citations

Semantic Scholar estimates that this publication has 66 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Mukherjee2015RegionBS, title={Region Based Segmentation in Presence of Intensity Inhomogeneity Using Legendre Polynomials}, author={Suvadip Mukherjee and Scott T. Acton}, journal={IEEE Signal Processing Letters}, year={2015}, volume={22}, pages={298-302} }