Model-based coupled denoising and segmentation of medical images

Ahmet Tuysuzoglu; Paulo Mendonca; Dirk Padfield

doi:10.1117/12.912618

24 February 2012 Model-based coupled denoising and segmentation of medical images

Ahmet Tuysuzoglu, Paulo Mendonca, Dirk Padfield

Proceedings Volume 8320, Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy; 83200B (2012) https://doi.org/10.1117/12.912618
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

Citation Download Citation

Ahmet Tuysuzoglu, Paulo Mendonca, and Dirk Padfield "Model-based coupled denoising and segmentation of medical images", Proc. SPIE 8320, Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy, 83200B (24 February 2012); https://doi.org/10.1117/12.912618

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