3D brain tumor localization and parameter estimation using thermographic approach on GPU

Bousselham, Abdelmajid; Bouattane, Omar; Youssfi, Mohamed; Raihani, Abdelhadi

doi:10.1016/j.jtherbio.2017.10.014

Cited by 28 publications

(8 citation statements)

References 32 publications

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“…The use of CUDA is not limited to solving the heat equation [14][15] [11] but several works that have used CUDA to solve other problems namely the parallel implementation of the hybrid intuitionistic fuzzy edge detection algorithm [23], the routing based on the neural network thanks to the parallelism exploit [24], the calculation of the correlation function of ensembles of Pseudo-random sequences formed automatically by CUDA program [25], and optimization of tasks that require massively parallel calculations to produce an effective implementation of the integral image algorithm [26].…”

Section: B Software Impactmentioning

confidence: 99%

“…The heat equation is the standard example of physical processes which can be modeled as a parabolic partial differential equation (PDE) [8], initially introduced in 1811 by Jean Baptiste Fourier. This problem can occur in one-dimensional [9], two-dimensional [10], threedimensional bioheat equation [11] or n-dimensional [12] physical objects. Solving the heat equation problems of paramount importance in physics, applied mathematics, engineering, and medicine.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

Belhaous¹,

Chokri²,

Baroud³

et al. 2021

JCOMSS

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Parallelization has become a universal technique for computing an intensive scientific simulation to shorten the execution time of complex problems. It consists of bringing together the power of several thousand processors to perform complex calculations at high speed. The choice of the runtime environment to execute parallel programs significantly influences the execution time. For this reason, this article aims to materialize the impact of computing architectures on the performance of parallel implementations. To better achieve this contribution, we have implemented the heat equation executed on CUDA platform and we have compared the results with those of SkelGIS implementation from the literature. Through the results of the experiments, we demonstrated that the execution time of the CUDA implementation on graphics processing unit (GPU) is almost 100X faster for very large meshes compared to the other implementations.

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Section: B Software Impactmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

Belhaous¹,

Chokri²,

Baroud³

et al. 2021

JCOMSS

View full text Add to dashboard Cite

show abstract

“…Since solving a partial differential equation (PDE) in a three-dimensional domain requires significant computational time, even with simplified models, combining DE with Pennes’ three-dimensional model significantly impacts performance. To reduce computational time and obtain solutions within a reasonable timeframe, we used general-purpose computing on graphics processing units (GPGPU) via the Compute Unified Device Architecture (CUDA) parallel computing platform to parallelize the implementation of the bioheat model [ 11 , 28 , 29 , 30 ], i.e., minimize the time spent evaluating the objective function.…”

Section: Introductionmentioning

confidence: 99%

A 3D Approach Using a Control Algorithm to Minimize the Effects on the Healthy Tissue in the Hyperthermia for Cancer Treatment

Fatigate

Lobosco

Reis

2023

Entropy

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According to the World Health Organization, cancer is a worldwide health problem. Its high mortality rate motivates scientists to study new treatments. One of these new treatments is hyperthermia using magnetic nanoparticles. This treatment consists in submitting the target region with a low-frequency magnetic field to increase its temperature over 43 °C, as the threshold for tissue damage and leading the cells to necrosis. This paper uses an in silico three-dimensional Pennes’ model described by a set of partial differential equations (PDEs) to estimate the percentage of tissue damage due to hyperthermia. Differential evolution, an optimization method, suggests the best locations to inject the nanoparticles to maximize tumor cell death and minimize damage to healthy tissue. Three different scenarios were performed to evaluate the suggestions obtained by the optimization method. The results indicate the positive impact of the proposed technique: a reduction in the percentage of healthy tissue damage and the complete damage of the tumors were observed. In the best scenario, the optimization method was responsible for decreasing the healthy tissue damage by 59% when the nanoparticles injection sites were located in the non-intuitive points indicated by the optimization method. The numerical solution of the PDEs is computationally expensive. This work also describes the implemented parallel strategy based on CUDA to reduce the computational costs involved in the PDEs resolution. Compared to the sequential version executed on the CPU, the proposed parallel implementation was able to speed the execution time up to 84.4 times.

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“…Yet, accelerating applications at the CPU level is still respectable compared to what is required in terms of computing. Therefore, many research fields were benefited from GPU acceleration techniques [ 10 , 14 , 22 , 39 , 40 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

Ali

Abbassi

Bouattane

2022

Multimed Tools Appl

Self Cite

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Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in human body. In research area, Fuzzy set is one of the most successful techniques that guarantees a robust classification. Spatial FCM (SFCM); one of the fuzzy c-means variants; considers spatial information to deal with the noisy images. To reduce this iterative algorithm’s execution time, a hard SIMD architecture has been planted named the Graphical Processing Unit (GPU). In this work, a great contribution has been done to diagnose, confront and implement three different parallel implementations on GPU. A parallel implementations’ extensive study of SFCM entitled PSFCM using 3 × 3 window is presented, and the experiments illustrate a significant decrease in terms of running time of this algorithm known by its high complexity. The experimental results indicate that the parallel version’s execution time is about 9.46 times faster than the sequential implementation on image segmentation. This gain in terms of speed-up is achieved on the Nvidia GeForce GT 740 m GPU.

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3D brain tumor localization and parameter estimation using thermographic approach on GPU

Cited by 28 publications

References 32 publications

Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

A 3D Approach Using a Control Algorithm to Minimize the Effects on the Healthy Tissue in the Hyperthermia for Cancer Treatment

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

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