Reducción de ruido en imágenes PET utilizando la NSCT y potenciales casi robustos (Denoising of PET Images using NSCT and Quasi-Robust Potentials)

Jose Manuel Mejia (jose.mejia@uacj.mx)1, Humberto Jesus Ochoa (hochoa@uacj.mx)1, Osslan Osiris Vergara (overgara@uacj.mx)1, Boris Mederos (boris.mederos@uacj.mx)1, vianey guadalupe cruz (vianey.cruz@uacj.mx)1


1Universidad autónoma de cuidad Juárez

This paper appears in: Revista IEEE América Latina

Publication Date: Aug. 2017
Volume: 15,   Issue: 8 
ISSN: 1548-0992


Abstract:
In this paper we present an algorithm for the denoising of small animal positron emission images. The proposed algorithm combines a multiresolution transform with robust filtering of regions. The image is processed in the non-subsampled contourlet domain, taking advantage of the transform ability to capture geometric information of important structures like small lesions and borders between tissues. Additionally, in the transform domain, we proposed to apply quasi‑ robust potentials in order to reduce the noise on regions without borders, this is done by estimating an edge map and a set of image regions. Finally the inverse contourlet transform is applied to obtain a denoised image. Quality tests using the NEMA NU4 2008 phantom show that the proposed method reduces the noise in the image while at the same time the average count is preserved on each region. Comparisons with other methods, using a contrast analysis on a simulated lesion show the superiority of our approach to denoise and preserve small structures such as lesions.

Index Terms:
Denoise,PET,NSCT,robust statistics   


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