Corpus ID: 14612040

Variational Level Set Segmentation and Bias Correction of Fused Medical Images

  title={Variational Level Set Segmentation and Bias Correction of Fused Medical Images},
  author={M. Renugadevi and Deepa Varghese and V. Vaithiyanathan and Nagarajan Raju},
  • M. Renugadevi, Deepa Varghese, +1 author Nagarajan Raju
  • Published 2012
  • Mathematics
  • Medical image fusion and segmentation has high impact on the digital image processing due to its spatial resolution enhancement and image sharpening. It has been used to derive useful information from the medical image data that provides the most accurate and robust method for diagnosis. This process is a compelling challenge due to the presence of inhomogeneities in the intensity of images. For addressing this challenge, the region based level set method is used for segmenting the fused… CONTINUE READING

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    Publications referenced by this paper.

    A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI

    Distance Regularized Level Set Evolution and Its Application to Image Segmentation


    Wavelet-based texture fusion of CT/MRI images

    Minimization of Region-Scalable Fitting Energy for Image Segmentation

    Implicit Active Contours Driven by Local Binary Fitting Energy


    Toward Objective Evaluation of Image Segmentation Algorithms

    A new intensity - hue - saturation fusion approach to image fusion with a tradeoff parameter

    • C. Kao, J. Gore, Z. Ding
    • IEEE T . Geosci . Remote
    • 2006

    A universal image quality index

    • Prince
    • IEEE Signal Process . Lett .
    • 2002