Statistical and heuristic image noise extraction (SHINE): a new method for processing Poisson noise in scintigraphic images

Hannequin, P.; Mas, J.

doi:10.1088/0031-9155/47/24/302

Cited by 52 publications

(56 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Alternatively, the noise in clinical PET images has been described using a Poisson + Gaussian model [22], where the presence of both, correlated and uncorrelated components, is assumed. Other studies [23][24][25][26] are aimed specifically at the reduction of Poisson noise contained in medical (including PET) images, exploiting its statistical properties. It is not clear if this approach is strictly valid in case of PET images [27].…”

Section: Introductionmentioning

confidence: 99%

Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm

et al. 2012

View full text Add to dashboard Cite

Noise levels observed in positron emission tomography (PET) images complicate their geometric interpretation. Post-processing techniques aimed at noise reduction may be employed to overcome this problem. The detailed characteristics of the noise affecting PET images are, however, often not well known. Typically, it is assumed that overall the noise may be characterized as Gaussian. Other PET-imaging-related studies have been specifically aimed at the reduction of noise represented by a Poisson or mixed Poisson + Gaussian model. The effectiveness of any approach to noise reduction greatly depends on a proper quantification of the characteristics of the noise present. This work examines the statistical properties of noise in PET images acquired with a GEMINI PET/CT scanner. Noise measurements have been performed with a cylindrical phantom injected with 11 C and well mixed to provide a uniform activity distribution. Images were acquired using standard clinical protocols and reconstructed with filtered-backprojection (FBP) and row-action maximum likelihood algorithm (RAMLA).Statistical properties of the acquired data were evaluated and compared to five noise models (Poisson, normal, negative binomial, log-normal, and gamma). Histograms of the experimental data were used to calculate cumulative distribution functions and produce maximum likelihood estimates for the parameters of the model distributions. Results obtained confirm the poor representation of both RAMLA-and FBP-reconstructed PET data by the Poisson distribution. We demonstrate that the noise in RAMLAreconstructed PET images is very well characterized by gamma distribution followed closely by normal distribution, while FBP produces comparable conformity with both normal and gamma statistics.

show abstract

Section: Introductionmentioning

confidence: 99%

Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm

et al. 2012

View full text Add to dashboard Cite

show abstract

“…However, Poisson distribution can be approximated by a gaussian distribution for a mean intensity roughly greater than 20. This is not always true in practice, but locally varying filters, such as our filter, have been shown to effectively reduce Poisson-type noise in low-count images (13,14).…”

Section: Discussionmentioning

confidence: 93%

Adaptive Autoregressive Model for Reduction of Poisson Noise in Scintigraphic Images

Takalo¹,

Hytti²,

Ihalainen³

2011

Journal of Nuclear Medicine Technology

View full text Add to dashboard Cite

In the present paper, a 2-dimensional adaptive autoregressive filter is proposed for noise reduction in images degraded with Poisson noise. In autoregressive models, each value of an image is regressed on its neighborhood pixel values, called the prediction region. The autoregressive models are linear prediction models that split an image into 2 additive components, a predictable image and a prediction error image. Methods: In this research, unfiltered images were split into smaller blocks, and best combinations of a prediction region and a block size for the image quality of predictable images were sought by using 3 Poisson noise-corrupted images with different image statistics. The images had dimensions of 128 · 128 pixels. Image quality was assessed by means of the mean squared error of the image. The adaptive autoregressive model was fitted into each block separately. Different degrees of overlapping of the image blocks were tested, and for each pixel the mean predictor coefficient of the different models was determined. The prediction error image was calculated for the entire image, and the filtered image was obtained by subtracting the prediction error image from the original image. The effect of the best adaptive autoregressive filter was illustrated using real scintigraphic data. Results: Generally, a prediction region of 4 orthogonal neighbors of the predicted pixel with a block size of 5 · 5 showed the best results. The use of 75% overlapping of the image blocks and 1 iteration of the filtering was found to improve prediction accuracy. The results were further improved when the 2 error term images were summed and subjected to adaptive autoregressive filtering and the resulting predictable image was added to the iteratively filtered image, allowing both noise reduction and edge preservation. Patient data illustrated effective noise reduction. Conclusion: The proposed method provided a convenient way to reduce Poisson noise in scintigraphic images on a pixel-by-pixel basis. Autoregressi ve modeling uses past values of a 1-dimensional signal (1) or neighborhood values of a 2-dimensional signal (2) to extract important information from a signal. The number of past or neighborhood values used is called the model order. A 2-dimensional autoregressive model can be regarded as a low-pass filter that divides the image into 2 additive components, a predictable image and a prediction error image. Ideally, the prediction errors in a prediction error image should obey gaussian noise. The counting statistics in a scintigraphic image obey Poisson distribution, but with a mean value greater than, say 20, the counting statistics can be approximated by gaussian distribution (3). Therefore, 2-dimensional autoregressive models are, in theory, suitable for noise reduction in scintigraphic images. In the present paper, a new 2-dimensional adaptive autoregressive model for filtering of scintigraphic images is introduced. The adaptive autoregressive filter was tested using an artificial organlike scintigraphic image (4) with ...

show abstract

“…However, existing techniques cannot be applied to the images used in this article, since generally they have intrinsic problems of low resolution, low contrast and low definition, asides from noise as an important factor in its deterioration [4,5]. The proposed method performs a preprocessing of the image, extracting numeric values, which highlights the zones with a high probability of containing a tumour or metastasis [6].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Abnormal Uptake through 123I-mIBG Scintigraphy Entropy for Paediatric Neuroblastoma Identification

Martínez-Díaz

Ruiz

et al. 2016

Entropy

View full text Add to dashboard Cite

Whole-body 123 I-Metaiodobenzylguanidine (mIBG) scintigraphy is used as primary image modality to visualize neuroblastoma tumours and metastases because it is the most sensitive and specific radioactive tracer in staging the disease and evaluating the response to treatment. However, especially in paediatric neuroblastoma, information from mIBG scans is difficult to extract because of acquisition difficulties that produce low definition images, with poor contours, resolution and contrast. These problems limit physician assessment. Current oncological guidelines are based on qualitative observer-dependant analysis. This makes comparing results taken at different moments of therapy, or in different institutions, difficult. In this paper, we present a computerized method that processes an image and calculates a quantitative measurement considered as its entropy, suitable for the identification of abnormal uptake regions, for which there is enough suspicion that they may be a tumour or metastatic site. This measurement can also be compared with future scintigraphies of the same patient. Over 46 scintigraphies of 22 anonymous patients were tested; the procedure identified 96.7% of regions of abnormal uptake and it showed a low overall false negative rate of 3.3%. This method provides assistance to physicians in diagnosing tumours and also allows the monitoring of patients' evolution.

show abstract

Statistical and heuristic image noise extraction (SHINE): a new method for processing Poisson noise in scintigraphic images

Cited by 52 publications

References 22 publications

Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm

Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm

Adaptive Autoregressive Model for Reduction of Poisson Noise in Scintigraphic Images

Recognition of Abnormal Uptake through 123I-mIBG Scintigraphy Entropy for Paediatric Neuroblastoma Identification

Contact Info

Product

Resources

About