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DECONVOLUTION REGULARIZED USING FUZZY C-MEANS ALGORITHM FOR BIOMEDICAL IMAGE DEBLURRING AND SEGMENTATION

Benoît Lelandais 1, * Frederic Duconge 1
* Corresponding author
1 LMN - Laboratoire des Maladies Neurodégénératives - UMR 9199
MIRCEN - Service MIRCEN : DRF/JACOB, CNRS - Centre National de la Recherche Scientifique : UMR 9199
Abstract : We address deconvolution and segmentation of blurry images. We propose to use Fuzzy C-Means (FCM) for regulariz-ing Maximum Likelihood Expectation Maximization decon-volution approach. Regularization is performed by focusing the intensity of voxels around cluster centroids during decon-volution process. It is used to deconvolve extremely blurry images. It allows us retrieving sharp edges without impact-ing small structures. Thanks to FCM, by specifying the desired number of clusters, heterogeneities are taken into account and segmentation can be performed. Our method is evaluated on both simulated and Fluorescence Diffuse Optical Tomography biomedical blurry images. Results show our method is well designed for segmenting extremely blurry images , and outperforms the Total Variation regularization approach. Moreover, we demonstrate it is well suited for image quantification.
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Submitted on : Tuesday, May 26, 2015 - 3:23:14 PM
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  • HAL Id : cea-01155379, version 1

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Benoît Lelandais, Frederic Duconge. DECONVOLUTION REGULARIZED USING FUZZY C-MEANS ALGORITHM FOR BIOMEDICAL IMAGE DEBLURRING AND SEGMENTATION. International Symposium on BIOMEDICAL IMAGING (ISBI): From Nano to Macro, Apr 2015, New-York, France. ⟨cea-01155379⟩

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