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Proceedings Paper

Model-based coupled denoising and segmentation of medical images
Author(s): Ahmet Tuysuzoglu; Paulo Mendonca; Dirk Padfield
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Paper Abstract

We present a new model-based framework for coupled segmentation and de-noising of medical images. The segmentation and de-noising steps are coupled through a discrete formulation of the total variation de-noising problem in a restricted setting such that each pixel in the image has its de-noised intensity level selected from a drastically reduced set of intensities. By creating such a reduced set of intensity levels, in which each intensity level represent the intensity across a region to be segmented, the intensity value for each de-noised pixel will be forced to assume a value in this limited set; by associating all pixels with the same de-noised value as a single region, image segmentation is naturally achieved. We derive two formulations corresponding to two noise models: additive white Gaussian and multiplicative Rayleigh. We furthermore show that the proposed framework enables globally optimal foreground/background segmentation of images with Rayleigh distributed noise.

Paper Details

Date Published: 25 February 2012
PDF: 6 pages
Proc. SPIE 8320, Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy, 83200B (25 February 2012); doi: 10.1117/12.912618
Show Author Affiliations
Ahmet Tuysuzoglu, GE Global Research (United States)
Boston Univ. (United States)
Paulo Mendonca, GE Global Research (United States)
Dirk Padfield, GE Global Research (United States)


Published in SPIE Proceedings Vol. 8320:
Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy
Johan G. Bosch; Marvin M. Doyley, Editor(s)

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