CPU and GPU behaviour modelling versus sequential and parallel bias field correction fuzzy C-means algorithm implementations

Cherradi, Bouchaib; Ali, Noureddine Ait; Abbassi, Ahmed El; Bouattane, Omar; Youssfi, Mohamed

doi:10.12988/ces.2017.612191

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…The elapsed time with all methods is given in Table . The proposed segmentation method (NCKMC) takes less time to find better results (1.899 seconds) than the other methods for segmentation and was better than the existing methods such as cuckoo‐based fuzzy clustering technique (CBFCT‐20.183 seconds), modified fuzzy c means (MFCM‐33.001 seconds), bias corrected fuzzy c means (BCFCM‐1492 seconds), or partial supervision fuzzy c means (PSFCM‐1671 seconds) . This is for segmentation .…”

Section: Discussionmentioning

confidence: 94%

“…The proposed segmentation method (NCKMC) takes less time to find better results (1.899 seconds) than the other methods for segmentation and was better than the existing methods such as cuckoo-based fuzzy clustering technique (CBFCT-20.183 seconds), 27 modified fuzzy c means (MFCM-33.001 seconds), 28 bias corrected fuzzy c means (BCFCM-1492 seconds), 29 or partial supervision fuzzy c means (PSFCM-1671 seconds). 30 This is for segmentation. 31 Finally, the results of the proposed method presented in Tables 3-5 are highlighted in bold as compared to conventional methods.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

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Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

Kollem

Reddy²,

Rao

2018

Int J Imaging Syst Tech

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At present, digital image processing plays a vital role in medical imaging areas and specifically in magnetic resonance imaging (MRI) of brain images such as axial and coronal sections. This article mainly focused on the MRI brain images. The existing methods such as total variation (MC), parallel MRI, modified pyramidal dual‐tree direction filter, adaptive dictionary selection algorithm, classifier methods, and fuzzy clustering techniques are poor in image eminence and precision. Thus, this article presents a novel approach consisting of denoising followed by segmentation. The objective of these proposed methods was visual eminence improvement of medical images to examine tumor extent using an adaptive partial differential equation (APDE)‐based analysis with soft threshold function in denoising. The fourth order, nonlinear APDE was used to denoise the image depending on gradient and Laplacian operators associated with the new adaptive Haar‐type wavelet transform. A second approach was the new convergent K‐means clustering for segmentation. The convergent K‐means procedure diminishes the summation of the squared deviations of structures in a cluster from the center. The significance of these proposed methods was to compute their performances in terms of mean squared error, peak signal‐to‐noise ratio, structure similarity, segmentation accuracy, false hit, missed‐term, and elapsed time. The results were analyzed with the MATLAB software.

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Section: Discussionmentioning

confidence: 94%

Section: Comparison With Other Methodsmentioning

confidence: 99%

Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

Kollem

Reddy²,

Rao

2018

Int J Imaging Syst Tech

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“…The method implemented in various GPU's and they reduced the computation time upto 52 times than the serial implementation. Cherradi et al, implemented a parallel BCFCM using GPU . They reach high computational gain while using the heterogeneous implementation of CPU and GPU.…”

Section: Related Workmentioning

confidence: 99%

Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine

Kalaiselvi

Sriramakrishnan

2018

Int J Imaging Syst Tech

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The proposed work introduces a modified method of fuzzy c means (FCM) algorithm using bias field correction and partial supervision techniques. The proposed method is named as bias corrected partial supervision FCM (BCPSFCM). The modified membership function takes the advantage of available knowledge from labeled patterns with the bias field correction. The experiment is tested on internet brain segmentation repository with their gold standard. The performance of the method is compared with three existing methods and 12 state of the art methods using dice coefficient, sensitivity, specificity, and accuracy. Accuracy of the proposed method reached upto 98%, 98%, and 99% of GM, WM, and CSF segmentation but required additional computation power from graphics processing unit (GPU). Further parallel BCPSFCM is proposed with the help of compute unified device architecture enabled GPU machine and the processing time is reduced up to 49 times than the serial implementation.

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CPU and GPU behaviour modelling versus sequential and parallel bias field correction fuzzy C-means algorithm implementations

Cited by 2 publications

References 18 publications

Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine

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