GPU based implementation of spatial fuzzy c-means algorithm for image segmentation

Aitali, N.; Cherradi, Bouchaib; Abbassi, Ahmed El; Bouattane, Omar; Youssfi, Mohamed

doi:10.1109/cist.2016.7805092

Cited by 15 publications

(6 citation statements)

References 13 publications

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“…In the objective function (10), ii and jj are the index of the ii data set and jj,   ik u ii are the partition matrix of the ii dataset. The parameter represents the degree of cooperation of the set jj with the set ii and has the value domain [0, 1], the value 0  = represents the data sets without the cooperation and is calculated according to the following formula: .…”

Section: A Collaborative Fuzzy Clustering Based On Gpusmentioning

confidence: 99%

Approach to collaborative fuzzy clustering in large data analysis

Mai¹,

Hop²,

Duc³

et al. 2023

ISJ

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Abstract— When data sets have one or more similar characteristics, the clustering in each of these data sets will have an effect on the other data sets. However, for various reasons such as data security issues, these data cannot be stored centrally but in different places. Collaborative clustering is a clustering technique that allows to performance of local clustering on each sub-data set and to exchange of information with other data sets. A collaborative process will be performed to adjust the clustering results on each subset to achieve better clustering results on the subsets of data. This paper presents a collaborative fuzzy clustering approach in big data analysis based on a high-performance computational model to improve the computation speed. Experiments on the Kitsune network attack dataset show that the proposed algorithm significantly improves the calculation speed compared to the previous method.

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Section: A Collaborative Fuzzy Clustering Based On Gpusmentioning

confidence: 99%

Approach to collaborative fuzzy clustering in large data analysis

Mai¹,

Hop²,

Duc³

et al. 2023

ISJ

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show abstract

“…One of the alternate powerful unsupervised classification technique is the fuzzy c-means algorithm [ 11 ] as several parallel implementations of this algorithm on GPUs have been proposed [ 2 , 7 , 8 ].…”

Section: Review Of Some Related Workmentioning

confidence: 99%

“…Indeed, this algorithm has already been parallelized on the GPU in [ 2 ]. Moreover, since the pixels at the edges do not have a neighborhood window eliminating them was thoughtful.…”

Section: Parallel Implementation Of the Sfcm Algorithmmentioning

confidence: 99%

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

Ali

Abbassi

Bouattane

2022

Multimed Tools Appl

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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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“…Likewise, due to the high resolution of the images provided by MRI scans, a lot of information about the brain structure and the brain tissues are easily provided, this has considerable advantages in the field of image analysis [7], [8]. There are four standard sequences used in MRI scans modalities: The T1-weighted MRI (T1), T2weighted MRI (T2), T1-weighted contrast-enhanced MRI (T1-CE), and FLAIR [9], [10]. In general, the T1W contrastenhanced is the most commonly used modality, because it allows an easy annotation of the healthy tissues [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection using MRI Images and Convolutional Neural Network

Lamrani¹,

Cherradi²,

Gannour³

et al. 2022

IJACSA

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A brain tumor is the cause of abnormal growth of cells in the brain. Magnetic resonance imaging (MRI) is the most practical method for detecting brain tumors. Through these MRIs, doctors analyze and identify abnormal tissue growth and can confirm whether the brain is affected by a tumor or not. Today, with the emergence of artificial intelligence techniques, the detection of brain tumors is done by applying the techniques and algorithms of machine learning and deep learning. The advantages of the application of these algorithms are the quick prediction of brain tumors, fewer errors, and greater precision, which help in decision-making and in choosing the most appropriate treatment for patients. In the proposed work, a convolution neural network (CNN) is applied with the aim of detecting the presence of a brain tumor and its performance is analyzed. The main purpose of this article is to adopt the approach of convolutional neural networks as a machine learning technique to perform brain tumor detection and classification. Based on training and testing results, the pre-trained architecture model reaches 96% in precision and classification accuracy rates. For the given dataset, CNN proves to be the better technique for predicting the presence of brain tumors.

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GPU based implementation of spatial fuzzy c-means algorithm for image segmentation

Cited by 15 publications

References 13 publications

Approach to collaborative fuzzy clustering in large data analysis

Approach to collaborative fuzzy clustering in large data analysis

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

Brain Tumor Detection using MRI Images and Convolutional Neural Network

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