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Abstract
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Article Information:
Variational Level Set Segmentation and Bias Correction of Fused Medical Images
M. Renugadevi, Deepa Varghese, V. Vaithiyanathan and N. Raju
Corresponding Author: M. Renugadevi
Submitted: February 16, 2012
Accepted: March 24, 2012
Published: April 30, 2012 |
Abstract:
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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 medical images with intensity
inhomogeneity. First, the IHS-PCA based fusion method is employed to fuse the images with intensity
inhomogeneity which is then filtered using the homomorphic filter. Then based on the model of the fused image
and the derived local intensity clustering property, the level set energy function is defined. This function is
minimized to simultaneously partition the image domain and to estimate the bias field for the intensity
inhomogeneity correction. The outstanding performance of this approach is illustrated using images of various
modalities. Experimental results highlight the effectiveness and advantage of this approach with the help of
various metrics and the results are found to be good and accurate.
Key words: Bias field, IHS (Intensity Hue Saturation), image fusion, intensity inhomogeneity, PCA (Principal Component Analysis), segmentation,
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Cite this Reference:
M. Renugadevi, Deepa Varghese, V. Vaithiyanathan and N. Raju, . Variational Level Set Segmentation and Bias Correction of Fused Medical Images. Asian Journal of Medical Sciences, (2): 66-74.
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ISSN (Online): 2040-8773
ISSN (Print): 2040-8765 |
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