The performances of iterative type-2 fuzzy C-mean on GPU for image segmentation

Ali, Noureddine Ait; Abbassi, Ahmed El; Cherradi, Bouchaib

doi:10.1007/s11227-021-03928-9

Cited by 25 publications

(6 citation statements)

References 28 publications

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“…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%

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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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Section: Review Of Some Related Workmentioning

confidence: 99%

“…Moreover their high computing capabilities Graphics Processing Units (GPU) are more adapted to image processing techniques [ 20 , 29 , 35 ]. Clustering algorithms not only can benefit from the parallel behavior of GPU processors but also can provide interesting performances [ 2 , 6 , 7 , 49 ].…”

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

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“…Furthermore, in the last three years of studies on higher-order types of FLS in particular, the designed and developed applications of interval type-2 fuzzy logic have increased significantly [48][49][50][51][52][53][54]. These type-2-based FLS applications have been identified in artificial intelligence (AI) [55][56][57][58][59], adaptive control [60][61][62][63][64][65][66], electric motor control [67][68][69][70][71][72], Internet of Things (IoT) [73][74][75][76][77], digital image processing [78][79][80][81][82][83][84] and other areas [85][86][87]. Of course, the application of interval type-2 fuzzy logic in the domain of control has recently attracted a lot of attention due to its better performance under uncertain conditions.…”

Section: Number Of Output Fuzzy Membership Functionsmentioning

confidence: 99%

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

2022

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This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.

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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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The performances of iterative type-2 fuzzy C-mean on GPU for image segmentation

Cited by 25 publications

References 28 publications

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

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

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

Brain Tumor Detection using MRI Images and Convolutional Neural Network

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