Joint solution for PET image segmentation, denoising, and partial volume correction

Xu, Ziyue; Gao, Mingchen; Papadakis, Georgios Z.; Luna, Brian; Jain, Sanjay; Mollura, Daniel J.; Bağcı, Ulaş

doi:10.1016/j.media.2018.03.007

Cited by 37 publications

(32 citation statements)

References 69 publications

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“…Medical imaging uses many different methods such as magnetic resonance (MR) imaging [1][2][3][4][5][6][7][8], radiography [4,[9][10][11], radionuclide [8,12], optical [11,13,14], ultrasound [1,15] and medical robotics [16,17]. The typical medical imaging system consists of three components (Figure 1): data acquisition, data consolidation and data processing.…”

Section: Introductionmentioning

confidence: 99%

“…Low dose radiation exposure for patient safety leads to noisy and low-contrast fluoroscopic sequences [11]. The reconstruction process of the positron emission tomography images includes inherent multiplicative noise, which prevents the analysis of visual data [12]. In optical CT for retinal imaging as another example use case, noise limits the measurement of structural features in the human eye, e.g., retinal layer properties [11].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

2020

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Denoising and compression of 2D and 3D images are important problems in modern medical imaging systems. Discrete wavelet transform (DWT) is used to solve them in practice. We analyze the quantization noise effect in coefficients of DWT filters for 3D medical imaging in this paper. The method for wavelet filters coefficients quantizing is proposed, which allows minimizing resources in hardware implementation by simplifying rounding operations. We develop the method for estimating the maximum error of 3D grayscale and color images DWT with various bits per color (BPC). The dependence of the peak signal-to-noise ratio (PSNR) of the images processing result on wavelet used, the effective bit-width of filters coefficients and BPC is revealed. We derive formulas for determining the minimum bit-width of wavelet filters coefficients that provide a high (PSNR ≥ 40 dB for images with 8 BPC, for example) and maximum (PSNR = ∞ dB) quality of 3D medical imaging by DWT depending on wavelet used. The experiments of 3D tomographic images processing confirmed the accuracy of theoretical analysis. All data are presented in the fixed-point format in the proposed method of 3D medical images DWT. It is making possible efficient, from the point of view of hardware and time resources, the implementation for image denoising and compression on modern devices such as field-programmable gate arrays and application-specific integrated circuits.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

2020

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“…This approach may hamper the performance of the NLM method as a similar pattern and information residing outside the search window cannot be exploited for effective noise removal. 18,19 The objective of this work was to propose a solution to this hurdle to enable efficient implementation of the NLM algorithm, particularly in 3D PET imaging. The proposed approach exploits a clustered version of the original image (based on different intensity levels and prominent edges) to guide the NLM method in finding similar patterns without adding to the overall computational burden of the method.…”

Section: Introductionmentioning

confidence: 99%

“…20 A similar idea has been applied in the quest of a joint solution for segmentation, smoothing and partial volume correction of PET images where the information extracted from segmented PET images is propagated through denoising and partial volume reduction. 19 In this work, we focus on the denoising of whole-body PET images by modification of the NLM method to make it particularly sensitive to meaningful small structures and edges using information residing in the clustered image. Moreover, an automated noise variance estimator was employed for efficient parameter setting in the NLM method based on the noise in the input image to establish an optimal compromise between noise removal and signal preservation.…”

Section: Introductionmentioning

confidence: 99%

Spatially guided nonlocal mean approach for denoising of PET images

Arabi

Zaidi

2020

Medical Physics

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Purpose: Nonlocal mean (NLM) filtering proved to be an effective tool for noise reduction in natural and medical imaging. The technique relies on existing redundant information in the input image to discriminate the genuine signal from noise. However, due to the prohibitively long computation time, the search for finding similar information is confined by a predefined search window, which may hamper the performance of this filter. In this work, a spatially guided non local mean (SG-NLM) approach was proposed to overcome this issue. The proposed method was evaluated on whole-body positron emission tomography images presenting with high noise levels, which adversely affect lesion detectability and quantitative accuracy. Methods: In the SG-NLM method, as opposed to the conventional NLM method, where a predefined search window is defined to confine exhaustive search for finding similar patterns, the information about similar patterns is extracted from the clustered version (created based on signal intensity levels) of the input image as well as information about prominent edges. The performance of the SG-NLM was evaluated against post-reconstruction NLM, Gaussian, bilateral and BayesShrink Wavelet denoising approaches. A digital phantom containing three small inserts mimicking lesions in the lung, experimental study using the Jaszczak phantom and whole-body PET/CT clinical studies were utilized to assess the performance of abovementioned denoising approaches. Results: The SG-NLM method led to a signal-to-noise (SNR) increase from 21.3 (unfiltered PET image) to 30.1 in computer simulations of small lesions while the NLM mean filer resulted in an SNR of 29.4 (P < 0.05). The experimental Jaszczak phantom study demonstrated that the contrastto-noise ratio (CNR) increased from 11.3 when using the Gaussian filter to 18.6 and 19.5 when using NLM and SG-NLM filters (P < 0.05), respectively. The superior performance of the SG-NLM approach was confirmed by clinical studies where the bias in malignant lesions decreased to À2.3 AE 1.1% compared to À11.7 AE 2.4 and À2.9 AE 1.1 achieved using the Gaussian and NLM methods (P < 0.05), respectively. Conclusions: The proposed SG-NLM achieves promising compromise between noise reduction and signal preservation compared to the conventional NLM method. The superior performance of the SG-NLM method was accomplished without adding extra burden to the computational complexity of the conventional NLM filter, which makes it attractive for denoising PET images.

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“…Image contrast enhancement is an essential pre-processing stage in image segmentation [1]. For several years, great effort has been devoted to the study of image enhancement techniques; wavelet-contourlet transform [2], iterative denoising and partial volume correction [3], iterative deconvolution [4] were few among them.…”

Section: Introductionmentioning

confidence: 99%

Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering

Hagos

Minh

Khawaldeh

et al. 2018

MPs

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Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation has remained a challenging problem due to the diverse image content, resolution, shape, and noise. This paper presents a fast positron emission tomography tumor segmentation method using superpixels. Principal component analysis is applied on the superpixels and their average value. The distance vector of each superpixel from the average is computed in the principal components coordinate system. Finally, k-means clustering is applied on the distance vector to recognize tumor and non-tumor superpixels. The proposed approach is implemented in MATLAB 2016A, and promising accuracy with execution time of 2.35 ± 0.26 s is achieved. Fast execution time is achieved since the number of superpixels, and the size of distance vector on which clustering was done are low compared to the number of pixels in the image.

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Joint solution for PET image segmentation, denoising, and partial volume correction

Cited by 37 publications

References 69 publications

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

Spatially guided nonlocal mean approach for denoising of PET images

Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering

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