Efficient parallelization on GPU of an image smoothing method based on a variational model

Gulo, Carlos A. S. J.; Arruda, Henrique Ferraz de; Araujo, Alex F. de; Sementille, Antônio Carlos; Tavares, João Manuel R. S.

doi:10.1007/s11554-016-0623-x

Cited by 14 publications

(8 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2016, Gulo, et al presented and discussed implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on GPU techniques. They suggested that the use of GPU techniques facilitate quick and efficient smoothing of images even when performed on large dimensional data sets [Gulo et al, (2016)]. In 2017, Bahri, et al introduced an algorithm to optimize the computing time of feature extraction methods for colour images.…”

Section: Related Workmentioning

confidence: 99%

“…It allows C programmers to write parallel codes for GPU using a few easy to use language extensions. The number of CUDA cores available for processing depends upon the graphic card's CUDA architecture [CUDA C programming Guide, (2019)], [Chakrabarti et al,(2012)], [Che et al, (2008)], [Nickolls et al, (2016) CUDA architecture is suitable for image processing applications (Zhiyi et al, 2008), (Y. K. Wang & Huang, 2014), (Kang et al, 2014), (Ferreiro et al, 2013), (J. , (Gulo et al, 2016). In this paper, to address the time complexity of NLM algorithm, a model for parallelization of NLM algorithm using CUDA is proposed.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Cuda Implementation of Nonlocal Means Algorithm for Gpu Processors

Wahid¹,

Sugandhi²,

Raju³

2020

INDJCSE

View full text Add to dashboard Cite

Non-Local Means algorithm (NLM) is a prominent image denoising algorithm. One of the major limitations of NLM algorithm and its variants is the time requirement. In this era of high performance computing, an efficient alternative to reduce the time complexity of any algorithm is its parallelization. In this paper, a parallelized version of basic NLM algorithm using CUDA architecture is proposed. The algorithm is developed on NVIDIA GeForce 940M GPU which follows Maxwell architecture with 3 SMs and 384 CUDA cores. Experiments are carried out using selected set of natural and medical images of various sizes. Our proposed parallelized version of NLM algorithm reduces the time requirement approximately by 50% in comparison to its basic version and also achieves comparable denoising performance in terms of PSNR, SSIM and FSIM evaluation metrics. The proposal is a model which can be customized for newer GPU architectures.

show abstract

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Cuda Implementation of Nonlocal Means Algorithm for Gpu Processors

Wahid¹,

Sugandhi²,

Raju³

2020

INDJCSE

View full text Add to dashboard Cite

show abstract

“…Noise reduction has been performed with different methods on different images . To determine the most appropriate algorithm for noise reduction (ie, to avoid poor de‐noising), the type of noise in the image should be identified.…”

Section: Antibody Validation By Image Analysismentioning

confidence: 99%

“…Noise reduction has been performed with different methods on different images. [16][17][18] To determine the most appropriate algorithm for noise reduction (ie, to avoid poor de-noising), the type of noise in the image should be identified. Therefore, we chose 3 regions (at least 100 × 100 pixels), which have homogenous (or close to homogenous) intensity values, from images and analyzed their histograms to identify noise type.…”

Section: Noise Modeling and Reductionmentioning

confidence: 99%

Quantitative validation of anti‐PTBP1antibody for diagnostic neuropathology use: Image analysis approach

Göçeri

Goksel

Elder

et al. 2017

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Problem Diagnostic neuropathology traditionally relies on subjective interpretation of visual data obtained from a brightfield microscope. This results in high variability, inability to multiplex, and unsatisfactory reproducibility even among experts. These diagnostic problems may affect patient outcomes and confound clinical decision-making. Furthermore, standard histological processing of pathological specimens results in auto-fluorescence and other artifacts, which have nearly blocked the implementation of fluorescent microscopy in diagnostic pathology. Thus, generation of objective and quantitative methodologies would augment the toolbox available to neuropathologists, which would ultimately enhance clinical decision making. Objective To develop image analysis methods to quantitatively validate anti-PTBP1 antibody for use in diagnostic neuropathology. Method We propose a computerized image analysis method to validate anti-PTBP1 antibody. Images were obtained from standard neuropathological specimens stained with anti-PTBP1 antibody. First, the noise characteristics of the images were modeled and images are de-noised according to the noise model. Next, images are filtered with sigma-adaptive Gaussian filtering for local normalization, and cell nuclei are detected and segmented with a k-means based deterministic approach. Result Experiments on 29 data sets from three cases of brain tumor (recurrent glioma, primary resections of glioblastoma harboring the EGFRVIII mutation, pilocytic astrocytoma) and reactive gliosis show statistically significant differences between the number of positively stained nuclei in images stained with and without anti-PTBP1 antibody (p values, t-test, are 40×10−4, 33×10−4, 6×10−4 and 46×10−3, respectively). Conclusion The experimental of analysis of specimens from three different brain tumor groups and one reactive gliosis group indicate the feasibility of using anti-PTBP1 antibody in diagnostic neuropathology and computerized image analysis provides a systematic and quantitative approach to explore feasibility.

show abstract

“…Gulo et al [34] described in their study how to use the high-performance computing CUDA-based architecture as a computational infrastructure to accelerate an algorithm for noise image removal. The parallel GPU-based implementation developed was compared against the corresponding sequential CPU-based implementation in several experiments.…”

Section: Image Filteringmentioning

confidence: 99%

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Gulo

Sementille

Tavares

2017

J Real-Time Image Proc

Self Cite

View full text Add to dashboard Cite

Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

show abstract

Efficient parallelization on GPU of an image smoothing method based on a variational model

Cited by 14 publications

References 41 publications

Cuda Implementation of Nonlocal Means Algorithm for Gpu Processors

Cuda Implementation of Nonlocal Means Algorithm for Gpu Processors

Quantitative validation of anti‐PTBP1antibody for diagnostic neuropathology use: Image analysis approach

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Contact Info

Product

Resources

About