Fusion of brushlet and wavelet denoising methods for nuclear images

Angelini, Elsa D.; Esser, Y.J.P.; Heertum, Ronald Van; Laine, Andrew F.

doi:10.1109/isbi.2004.1398756

Cited by 8 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional filtering operations in frequency domain [2], such as Butterworth filter, are utilized as image denoising methods incorporated in SPECT γ -camera workstations. Although multi-scale denoising methods have been proposed [12][13][14][15][16][17][18][19][20][21][22][23], their performance has not been fully investigated. In this study, the performance of the multi-scale platelet denoising method was compared with the wellestablished Butterworth filter [2,24] applied either as a pre-or post-processing step [36] on images reconstructed without and/or with AC.…”

Section: Discussionmentioning

confidence: 99%

“…Sophisticated methods are required for treating the coefficients in the wavelet domain, such as applying SNRadaptive nonlinear thresholding [12,13] or multiresolution regularization schemes [15][16][17] to noisy wavelet coefficients. Another category of multi-scale denoising methods is based on the estimation of Poisson-distributed data at all resolutions, such as application of a Bayesian multi-scale estimator in a Haar wavelet framework [18] and multi-scale linear hard thresholding supported by wavelet-packet [19] and brushlet [20,21] expansions. Recently, a platelet multi-scale denoising method utilizing maximum penalized likelihood estimation for noise threshold derivation was proposed, with promising performance [22,23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating image denoising methods in myocardial perfusion single photon emission computed tomography (SPECT) imaging

Skiadopoulos¹,

Karatrantou²,

Korfiatis³

et al. 2009

Meas. Sci. Technol.

View full text Add to dashboard Cite

The statistical nature of single photon emission computed tomography (SPECT) imaging, due to the Poisson noise effect, results in the degradation of image quality, especially in the case of lesions of low signal-to-noise ratio (SNR). A variety of well-established single-scale denoising methods applied on projection raw images have been incorporated in SPECT imaging applications, while multi-scale denoising methods with promising performance have been proposed. In this paper, a comparative evaluation study is performed between a multi-scale platelet denoising method and the well-established Butterworth filter applied as a pre- and post-processing step on images reconstructed without and/or with attenuation correction. Quantitative evaluation was carried out employing (i) a cardiac phantom containing two different size cold defects, utilized in two experiments conducted to simulate conditions without and with photon attenuation from myocardial surrounding tissue and (ii) a pilot-verified clinical dataset of 15 patients with ischemic defects. Image noise, defect contrast, SNR and defect contrast-to-noise ratio (CNR) metrics were computed for both phantom and patient defects. In addition, an observer preference study was carried out for the clinical dataset, based on rankings from two nuclear medicine clinicians. Without photon attenuation conditions, denoising by platelet and Butterworth post-processing methods outperformed Butterworth pre-processing for large size defects, while for small size defects, as well as with photon attenuation conditions, all methods have demonstrated similar denoising performance. Under both attenuation conditions, the platelet method showed improved performance with respect to defect contrast, SNR and defect CNR in the case of images reconstructed without attenuation correction, however not statistically significant (p > 0.05). Quantitative as well as preference results obtained from clinical data showed similar performance of the denoising methods studied. In conclusion, the multi-scale platelet denoising method applied on raw projection images provides more efficient noise reduction while preserving image quality in a myocardial phantom SPECT imaging as compared to the Butterworth filter applied either on projection or reconstructed images. However, this trend in favour of the platelet denoising method was not observed on clinical data reconstructed either without or with attenuation correction.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating image denoising methods in myocardial perfusion single photon emission computed tomography (SPECT) imaging

Skiadopoulos¹,

Karatrantou²,

Korfiatis³

et al. 2009

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The number of operator combinations were 2 8 (256), 2 12 (4096), 2 12 (4096), 2 18 (262,144), 2 12 (4096), 2 18 (262,144), 2 18 (262,144), and 2 27 (134,217,728). The principle of matrix convolution was used to perform the operation in the morphological structure matrix.…”

Section: Morphological Structure Operationmentioning

confidence: 99%

“…Previous studies have shown that it is possible to remove strike artifacts using interpolation of projections by contouring, but it is challenging to select an interpolated value. For instance, methods such as the Brushlet, Wavelet, and Curvelet transformation can be used to remove strike artifacts from PET images; however, caution should be applied when selecting a threshold [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Efficient Strike Artifact Reduction Based on 3D-Morphological Structure Operators from Filtered Back-Projection PET Images

Chiu

Huang

et al. 2021

Sensors

View full text Add to dashboard Cite

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole-body to effectively detect tumor presence and distribution. The filtered back-projection (FBP) algorithm is one of the most common images reconstruction methods. However, it will generate strike artifacts on the reconstructed image and affect the clinical diagnosis of lesions. Past studies have shown reduction in strike artifacts and improvement in quality of images by two-dimensional morphological structure operators (2D-MSO). The morphological structure method merely processes the noise distribution of 2D space and never considers the noise distribution of 3D space. This study was designed to develop three-dimensional-morphological structure operators (3D MSO) for nuclear medicine imaging and effectively eliminating strike artifacts without reducing image quality. A parallel operation was also used to calculate the minimum background standard deviation of the images for three-dimensional morphological structure operators with the optimal response curve (3D-MSO/ORC). As a result of Jaszczak phantom and rat verification, 3D-MSO/ORC showed better denoising performance and image quality than the 2D-MSO method. Thus, 3D MSO/ORC with a 3 × 3 × 3 mask can reduce noise efficiently and provide stability in FBP images.

show abstract

“…and computed tomography. Since it is impractical to cover all of these applications in this paper, the topics of fusion [6][7][8][9][10][11][12][13][14][15][16] was chosen for its value in introduce. analysis.…”

Section: Introductionmentioning

confidence: 99%

Medical image processing using wavelets

Zhang

Dai

2006

SPIE Proceedings

View full text Add to dashboard Cite

Wavelets have been widely used in signal and image processing for the past 20 years. In this paper. we present an overview of the various methods of the wavelet transform in medical image fusion. Using wavelet transform. we presented a general image fusion scheme involves decomposition of the input images, calculation of the wavelet transform modulus. fusion strategy. and reconstruction for new fusion image. Next we provide a survey of recent wavelet developments in medical image fusion. In each case. we provide the reader with some genera! background information and a brief explanation of how the methods work.

show abstract

Fusion of brushlet and wavelet denoising methods for nuclear images

Cited by 8 publications

References 9 publications

Evaluating image denoising methods in myocardial perfusion single photon emission computed tomography (SPECT) imaging

Evaluating image denoising methods in myocardial perfusion single photon emission computed tomography (SPECT) imaging

Efficient Strike Artifact Reduction Based on 3D-Morphological Structure Operators from Filtered Back-Projection PET Images

Medical image processing using wavelets

Contact Info

Product

Resources

About