Speckle reducing bilateral filter for cattle follicle segmentation

Tang, Jinshan; Guo, Sen; Sun, Qingling; Deng, Youping; Zhou, Dongfeng

doi:10.1186/1471-2164-11-s2-s9

Cited by 25 publications

(17 citation statements)

References 27 publications

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“…Its principal virtues have widely been recognised in 2D image processing [9,12,13] and, more recently, in medical imaging as well [2-6], but applications in Nuclear Medicine in general and PET in particular seem to have been missing up to now (we have found only one exception [14], where BF was mentioned as a preprocessing step for a new image segmentation algorithm).…”

Section: Discussionmentioning

confidence: 99%

“…Such filters are typically used in 2D image processing and were first introduced by Tomasi and Manduchi [1]. BF have already been used in medical imaging, notably in MRI [2-4] and ultrasound imaging [5,6]. Their potential usefulness has also been recognised in the field of Nuclear Medicine [7], and bilateral filtering has been proposed for improvement of iterative image reconstruction in PET [8].…”

Section: Introductionmentioning

confidence: 99%

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Suitability of bilateral filtering for edge-preserving noise reduction in PET

et al. 2011

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BackgroundTo achieve an acceptable signal-to-noise ratio (SNR) in PET images, smoothing filters (SF) are usually employed during or after image reconstruction preventing utilisation of the full intrinsic resolution of the respective scanner. Quite generally Gaussian-shaped moving average filters (MAF) are used for this purpose. A potential alternative to MAF is the group of so-called bilateral filters (BF) which provide a combination of noise reduction and edge preservation thus minimising resolution deterioration of the images. We have investigated the performance of this filter type with respect to improvement of SNR, influence on spatial resolution and for derivation of SUVmax values in target structures of varying size.MethodsData of ten patients with head and neck cancer were evaluated. The patients had been investigated by routine whole body scans (ECAT EXACT HR+, Siemens, Erlangen). Tomographic images were reconstructed (OSEM 6i/16s) using a Gaussian filter (full width half maximum (FWHM): Γ0 = 4 mm). Image data were then post-processed with a Gaussian MAF (FWHM: ΓM = 7 mm) and a Gaussian BF (spatial domain: ΓS = 9 mm, intensity domain: ΓI = 2.5 SUV), respectively. Images were assessed regarding SNR as well as spatial resolution. Thirty-four lesions (volumes of about 1-100 mL) were analysed with respect to their SUVmax values in the original as well as in the MAF and BF filtered images.ResultsWith the chosen filter parameters both filters improved SNR approximately by a factor of two in comparison to the original data. Spatial resolution was significantly better in the BF-filtered images in comparison to MAF (MAF: 9.5 mm, BF: 6.8 mm). In MAF-filtered data, the SUVmax was lower by 24.1 ± 9.9% compared to the original data and showed a strong size dependency. In the BF-filtered data, the SUVmax was lower by 4.6 ± 3.7% and no size effects were observed.ConclusionBilateral filtering allows to increase the SNR of PET image data while preserving spatial resolution and preventing smoothing-induced underestimation of SUVmax values in small lesions. Bilateral filtering seems a promising and superior alternative to standard smoothing filters.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Suitability of bilateral filtering for edge-preserving noise reduction in PET

et al. 2011

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“…The other filter operates in the intensity domain, where the filtering weight is calculated based on the difference between the intensity values of the origin pixel and those of its neighboring pixels. Tang et al analyzed the behavior of filtering in the intensity dimension of a conventional bilateral filter for multiplicative noise 4 . He found that the intensity difference between two pixels was not only related to the difference of the noise but was also related to the intensity of the pixels in the homogeneous region of speckle-corrupted images.…”

Section: Normalized Bilateral Filtermentioning

confidence: 99%

“…Bilateral filters, another type of popular nonlinear filters, have received a lot of attention in terms of speckle reduction recently due to their ability to suppress noise suppression and preserve edges 3 . Tang et al modified a conventional bilateral filter by introducing a normalized difference in the intensity domain 4 . The filter developed by Tang et al 4 , referred to as a normalized bilateral filter, can be directly applied to speckle reduction in ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

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3D cluster-driven trilateral filter for speckle reduction in ultrasound images

Tang

2015

SPIE Proceedings

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We propose a cluster-driven trilateral filter for speckle reduction in ultrasound images. In addition to operating in the spatial dimension and the intensity dimension, the proposed filter also merges clustering information simultaneously. We compare the proposed filter with a normalized bilateral filter for speckle reduction using real 3-D ultrasound images. Our experimental results indicate that the cluster-driven trilateral filter exhibits better performance for speckle reduction and edge feature preservation than the normalized bilateral filter. In addition, we investigate the graphic processing unit (GPU) technique and apply it to the proposed 3-D filter. We design and test a GPU framework and compare it with a single-core CPU framework. Our experimental results show that the GPU-accelerated trilateral filter can obtain a roughly 20-fold increase in speed.

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Uncertainty‐guided self‐ensembling model for semi‐supervised segmentation of multiclass retinal fluid in optical coherence tomography images

Liu

Wang

Cao

et al. 2021

Int J Imaging Syst Tech

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Macular edema is the accumulation of fluid leakage from retinal capillaries.Optical coherence tomography (OCT) images can show changes in the retinal tissue caused by ophthalmological diseases, such as fluid accumulation. Therefore, the segmentation of retinal fluid is important. Some methods based on image processing and machine learning often require large amounts of labeled data and rich domain knowledge. This study proposes a self-ensembling semisupervised model based on uncertainty guidance, namely, UGNet. The model is trained end-to-end with a few labeled data and plenty of unlabeled data, and contains a teacher model and a student model with the same architecture. The two models consist of an encoder and three decoders, which are used to predict the probability map, contour map, and distance map. The segmentation result is the fusion result of the three maps generated by the student model. The selective kernel module (SKM) is embedded in the decoder to make the model adaptively adjust the receptive field according to the size of the fluid. The uncertainty of teacher model evaluation guides the student model to learn more reliable knowledge. The proposed method is trained and evaluated on the RETOUCH challenge dataset. The experimental results show that our method achieves better segmentation results than other start-of-the-art methods.

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Speckle reducing bilateral filter for cattle follicle segmentation

Cited by 25 publications

References 27 publications

Suitability of bilateral filtering for edge-preserving noise reduction in PET

Suitability of bilateral filtering for edge-preserving noise reduction in PET

3D cluster-driven trilateral filter for speckle reduction in ultrasound images

Uncertainty‐guided self‐ensembling model for semi‐supervised segmentation of multiclass retinal fluid in optical coherence tomography images

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