Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction

2005 ◽  
Vol 15 (5) ◽  
pp. 455-465 ◽  
Author(s):  
Ashish Thakur ◽  
R.S. Anand
Keyword(s):  
Image Quality ◽  
Comparative Evaluation ◽  
Speckle Reduction ◽  
Wavelet Filters

GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging

2017 ◽  
Vol 39 (4) ◽  
pp. 240-259 ◽  
Author(s):  
Tiexiang Wen ◽  
Ling Li ◽  
Qingsong Zhu ◽  
Wenjian Qin ◽  
Jia Gu ◽  
...  
Keyword(s):  
Image Quality ◽  
Ultrasound Imaging ◽  
Kernel Regression ◽  
3D Ultrasound ◽  
Speckle Reduction ◽  
Reconstruction Method ◽  
Processing Unit ◽  
Hole Filling ◽  
Volume Reconstruction ◽  
Computational Performance

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor–based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of [Formula: see text] and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.

Restoration and Enhancement of Digitally Reconstructed Holographic Images

2011 ◽  
pp. 105-120
Author(s):  
Rajeev Srivastava
Keyword(s):  
Image Quality ◽  
Image Data ◽  
Speckle Noise ◽  
Zero Order ◽  
Speckle Reduction ◽  
Average Intensity ◽  
Robust Algorithms ◽  
Order Diffraction ◽  
High Pass

Holograms can be reconstructed optically or digitally with the use of computers and other related devices. During the reconstruction phase of a hologram by optical or digital methods, some errors may also be introduced that may degrade the quality of obtained hologram, and may lead to a misinterpretation of the holographic image data, which may not be useful for particular application. The basic common errors are zero-order diffraction and speckle noise. These errors have more undesirable effects in digital than in optical holography because the systems of recording and visualization used in the digital holography are extremely sensitive to them or inclusively increase them. The zero-order diffraction can be removed by using high pass filters with low cut-off frequencies and by subtracting the average intensity of all pixels of the hologram image from the original hologram image. Further, the speckle noise introduced during the formation of digital holographic images, which is multiplicative in nature, reduces the image quality, which may not be suitable for specific applications. As the range of applications get broader, demands toward better image quality increases. Hence, the suppression of noise, higher resolution of the reconstructed images, precise parameter adjustment, and faster, more robust algorithms are the essential issues. In this chapter, the various methods available in literature for enhancement and speckle reduction of digital holographic images have been discussed, and a comparative study of results has been presented.

A novel reconstruction tool (syngo DynaCT Head Clear) in the post-processing of DynaCT images to reduce artefacts and improve image quality

2016 ◽  
Vol 8 (12) ◽  
pp. 1268-1272 ◽  
Author(s):  
Stephanie Lescher ◽  
Christina Reh ◽  
Maya Christina Hoelter ◽  
Katja Czeppan ◽  
Luciana Porto ◽  
...  
Keyword(s):  
Image Quality ◽  
Comparative Evaluation ◽  
Reconstruction Algorithm ◽  
Field Of View ◽  
Reconstruction Algorithms ◽  
Point Scale ◽  
Flat Detector ◽  
Improve Image Quality ◽  
Standard Algorithm ◽  
Advanced Algorithm

BackgroundLatest generations of flat detector (FD) neuroangiography systems are able to obtain CT-like images of the brain parenchyma. Owing to the geometry of the C-arm system, cone beam artifacts are common and reduce image quality, especially at the periphery of the field of view. An advanced reconstruction algorithm (syngo DynaCT Head Clear) tackles these artifacts by using a modified interpolation-based 3D correction algorithm to improve image quality.Materials and methodsEleven volumetric datasets from FD-CT scans were reconstructed with the standard algorithm as well as with the advanced algorithm. In a two-step data analysis process, two reviewers compared dedicated regions of the skull and brain in both reconstruction modes using a 5-point scale (1, much better; 5, much worse; advanced vs standard algorithm). Both reviewers were blinded to the reconstruction mode. In a second step, two additional observers independently evaluated image quality of the 3D data (non-comparative evaluation) in dedicated regions also using a 5-point scale (1, not diagnostically evaluable; 5, good quality, perfectly usable for diagnosis) for both reconstruction algorithms.ResultsBoth in the comparative evaluation of dedicated brain regions and in the independent analysis of the FD-CT datasets the observers rated a better image quality if the advanced algorithm was used. The improvement in image quality was statistically significant at both the supraganglionic (p=0.018) and the infratentorial (p=0.002) levels.ConclusionsThe advanced reconstruction algorithm reduces typical artifacts in FD-CT images and improves image quality at the periphery of the field of view.

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