A Nonlinear Probabilistic Curvature Motion Filter for Positron Emission Tomography Images

Alrefaya, Musa; Sahli, Hichem; Vanhamel, Iris; Hào, Ðinh Nho

doi:10.1007/978-3-642-02256-2_18

Cited by 5 publications

(11 citation statements)

References 20 publications

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“…The probabilistic curvature motion filter with a diffusivity function that consider global parameter (constant threshold), presented in [12] does not consider the images with spatially varying noise levels such as sinograms. The diffusivity function used in [12] has a global threshold parameter which is related to the image noise standarddeviation T = σ n .…”

Section: Spatially Adaptive Bayesian Diffusivity Functionmentioning

confidence: 99%

“…Let m denote the ideal, noise-free gradient magnitude and define the following two hypotheses: H 0 : "an edge element of interest is absent" and H 1 :"edge element of interest is present" precisely as: H 0 : m ≤ σ, and H 1 : m > σ. The noise level σ is estimated using wavelet based method where the noise is reconstructed from wavelet coefficients at the finest level of detail, as presented in [12]. The diffusivity function is defined as:…”

Section: The Probabilistic Diffusivitymentioning

confidence: 99%

“…In our previous work [12], we have proposed and investigated a Probabilistic Curvature Motion filter for enhancing the PET image by filtering the PET raw data (sinogram) and the results were very promise. The proposed method was initiated based on (i) the curvature motion method [4], and (ii) the probabilistic diffusivity function [3], and considering the following PET images characteristics [12]:…”

Section: Introductionmentioning

confidence: 97%

“…The proposed method was initiated based on (i) the curvature motion method [4], and (ii) the probabilistic diffusivity function [3], and considering the following PET images characteristics [12]:…”

Section: Introductionmentioning

confidence: 99%

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Adaptive nonlinear probabilistic filter for Positron Emission Tomography

Alrefaya

Sahli

2012

2012 IEEE 12th International Conference on Bioinformatics &Amp; Bioengineering (BIBE)

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Radiologists face difficulties when reading and interpreting Positron Emission Tomography (PET) images because of the high noise level in the raw-projection data (i.e. the sinogram). The later may lead to erroneous diagnoses. Aiming at finding a suitable denoising technique for PET images, in our first work, we investigated filtering the sinogram with a constraint curvature motion filter where we computed the edge stopping function in terms of edge probability under a marginal prior on the noise free gradient. In this paper, we show that the Chi-square is the appropriate prior for finding the edge probability in the sinogram noise-free gradient. Since the sinogram noise is uncorrelated and follows a Poisson distribution, we then propose an adaptive probabilistic diffusivity function where the edge probability is computed at each pixel. We demonstrate quantitatively and qualitatively through simulations that the performance of the proposed method substantially surpasses that of state-of-art methods, both visually and in terms of statistical measures.

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Section: Spatially Adaptive Bayesian Diffusivity Functionmentioning

confidence: 99%

Section: The Probabilistic Diffusivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Adaptive nonlinear probabilistic filter for Positron Emission Tomography

Alrefaya

Sahli

2012

2012 IEEE 12th International Conference on Bioinformatics &Amp; Bioengineering (BIBE)

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“…However, such images intrinsically contain statistical noise, which sometimes makes it difficult to decide whether or not there is an abnormal uptake. PET images are produced by reconstructing raw sinogram data acquired from a scanner [3]. Filtered back projection (FBP) used to be the algorithm of choice for PET image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Visual and Quantitative Assessment of a New Anisotropic Diffusion Filter (Statistical Transfer with Optimizing Noise and Edge Sensing) for Positron Emission Tomography

Iizuka¹,

Kaneta²,

Ogawa³

et al. 2017

J Mol Imaging Dynam

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Post-filtering with a Gaussian filter is commonly used to reduce noise in positron emission tomography (PET) images. However, its non-selective smoothing obscures the edges of lesions or organs. We compared the performance of a newly developed anisotropic diffusion filter called "Statistical Transfer with Optimizing Noise and Edge Sensing" (STONES) with that of the Gaussian filter for small lesions on PET images. We selected seven PET/ computed tomography (CT) image slices of the lungs from three patients with multiple lung metastases. For each slice, the lesion detection rates by two physicians (A and B) were compared for Gaussian-and STONES-filtered PET images. The maximum standardized uptake (SUV max ) values of the detected lesions were also compared for non-, Gaussian-, and STONES-filtered images. Physician A detected 19 lesions in the Gaussian-filtered images and 23 lesions in the STONES-filtered images, while Physician B detected 14 lesions in the Gaussian-filtered images and 19 lesions in the STONES-filtered images. SUV max for the STONES-filtered images was significantly higher and closer to that of the non-filtered images compared to those for the Gaussian-filtered images. STONES improved the detection rate and increased SUV max in comparison with Gaussian filter. Thus, it should be more advantageous for the detection of small lesions with PET.

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