Region Based Segmentation in Presence of Significant Intensity Inhomogeneity using Legendre Polynomials

Abstract

we propose a region based segmentation method which is capable of segmenting objects in presence of significant intensity inhomogeneity. Present method use some form of local processing to tackle intra region inhomogeneity, which further makes such methods susceptible to local minima. Here we present a framework which generalizes the traditional Chan-Vese algorithm. In contrast with existing local techniques, we represent the illumination of the regions of interest in a lower dimensional subspace using set of pre-specified basis functions. This representation can accommodate heterogeneous objects, even in presence of noise. We compare our results with several techniques like biological/biomedical images with tubular or filamentous structures. As the, we achieve a 44% increase in performance, which demonstrates efficacy of the method.

Authors and Affiliations

M. Rangaswamy, M. Sunitha, M. MD. Zaid Khan, D. Pavani, J. Naga Ratnam

Keywords

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  • EP ID EP23786
  • DOI http://doi.org/10.22214/ijraset.2017.4079
  • Views 239
  • Downloads 7

How To Cite

M. Rangaswamy, M. Sunitha, M. MD. Zaid Khan, D. Pavani, J. Naga Ratnam (2017). Region Based Segmentation in Presence of Significant Intensity Inhomogeneity using Legendre Polynomials. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 5(4), -. https://europub.co.uk/articles/-A-23786