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A fractional-order PDE-based contour detection model with CeNN scheme for medical images

Abstract

This paper introduces a contour detection scheme to detect object contours in medical images. A new PDE model is designed by including a fractional-order regularization term, making it robust against noise and maintaining the regularity of level set function (LSF) during evolution. A cellular neural network (CeNN) model is used to solve the proposed contour detection PDE. The main advantages of using the CeNN-based approach are that it wipes out the requirement of a reinitialization of level set and can be implemented efficiently on parallel chips. Finally, an experimental study is carried out, which exhibits the feasibility of the proposed approach in contour detection from a set of medical images.

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Acknowledgements

One of the authors Mahima is thankful the support of University Grant Commision (UGC) during her Ph.D through Ref. No. 412261. This work is also partially supported by the project Grant No. DST/INT/CZECH/P-10/2019 under Indo-Czech Bilateral Research Program.

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Correspondence to Sanjeev Kumar.

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Lakra, M., Kumar, S. A fractional-order PDE-based contour detection model with CeNN scheme for medical images. J Real-Time Image Proc (2021). https://doi.org/10.1007/s11554-021-01172-1

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Keywords

  • Cellular neural network
  • Contour detection
  • Fractional order model
  • Image segmentation
  • Level set evolution