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Papers by Mohammed Debakla

Noise reduction is a very important task in image processing. In this aim, many approaches and me... more Noise reduction is a very important task in image processing. In this aim, many approaches and methods have been developed and proposed in the literature. In this paper, we present a new restoration method for noisy images by minimizing the Total Variation (TV) under constraints using a multilayer neural network (MLP). Indeed, the obtained Euler-Lagrange functional is resolved by minimizing an error functional. The MLP parameters (weights) in this case are adjusted to minimize appropriate functional and provides optimal solution. The proposed method can restore degraded images and preserves the discontinuities. The effectiveness of our a pproach has been tested on synthetic and real images, and compared with known restoration methods

To reduce the Gaussian noise from Magnetic Resonance Image (MRI) corrupted during their acquisiti... more To reduce the Gaussian noise from Magnetic Resonance Image (MRI) corrupted during their acquisition process, we propose a filtering method based RBF neural network. Indeed, the Gaussian noise is considered and formulated as constraints in an energy functional base on minimization of Total Variation (TV). In the RBF training stage, the backprobagation algorithm is used to solve the TV functional energy, where the reaches image is its solution. The considered filter has given good results of noise removal when compared to other approaches.

International Journal of Grid and High Performance Computing, 2018

This article describes how the idea of a hybrid cloud comes from the coupling of public and priva... more This article describes how the idea of a hybrid cloud comes from the coupling of public and private clouds to more efficiently address user requirements. This article addresses the problem of resource provisioning in hybrid cloud. This article is mainly concerned about optimizing the resources provisioning task through the reduction of the tasks completion time together with minimal cost and more reliable services. Two steps are considered in the proposed model, which are brokering and scheduling. In the brokering strategy, this article formalizes the problem as a minimization problem of the completion time as the objective function, under cost and service reliability constraints. The scheduling strategy contains two phases: (i) use the balanced k-means method to classify the submitted tasks and, (ii) perform a minimum assignment using the Hungarian algorithm. The proposed model is evaluated within the simulation framework CloudSim. Experimental results demonstrate that the provisio...

International Journal of Computers and Applications, 2019

One of emerging challenges in Medical image analysis is clustering. Fuzzy C-means (FCM) algorithm... more One of emerging challenges in Medical image analysis is clustering. Fuzzy C-means (FCM) algorithm is one of the most popular clustering algorithms because it is efficient, straightforward, and easy to implement. However, FCM is sensitive to initialization and is easily trapped in local optima, such a drawback could be overcome by evolutionary algorithms. This paper is dedicated to implement a fuzzy strategy evolutionary approach based to optimize the centers of the clusters by minimizing the objective function of the FCM algorithm. This approach is based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm to find the optimum values of the centers of the clusters in order to classify Magnetic Resonance Imaging (MRI) brain images. The proposed approach has been validated against both simulated and clinical MRI and it has yield competitive results when compared to FCM algorithms. Results show that the proposed algorithm has obtained reasonable segmentation of white matter, gray matter, and cerebrospinal fluid from MRI data.

IET Image Processing, 2019

Image clustering is considered amongst the most important tasks in medical image analysis and it ... more Image clustering is considered amongst the most important tasks in medical image analysis and it is regularly required as a starter and vital stage in the computer-aided medical image process. In brain magnetic resonance imaging (MRI) analysis, image clustering is regularly used for estimating and visualising the brain anatomical structures, to detect pathological regions and to guide surgical procedures. This study presents a new method for MRI brain images clustering based on the farthest point first algorithm and fuzzy clustering techniques without using any a priori information about the clusters number. The algorithm has been approved against both simulated and clinical magnetic resonance images and it has been compared with the fourth clustered algorithms. Results demonstrate that the proposed algorithm has given reasonable segmentation of white matter, grey matter and cerebrospinal fluid from MRI data, which is superior in preserving image details and segmentation accuracy compared with the other four algorithms giving more than 91% in Jaccard similarity.

International Journal of Internet Technology and Secured Transactions, 2020

Computational grids have the potential for solving large-scale scientific problems using heteroge... more Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, the characteristics of dynamicity, resource heterogeneity and scalability have made fault tolerance more complex. In this paper, we propose FT-GRC a fault tolerance model that seeks to find the best substitute for the failed node by the clustering of the grid resources. This model is based on dynamic coloured graphs without replication of computer resources. The proposed fault tolerance mechanism uses scoring function to determine the appropriate substitute for each failed node by calculating the performance level of each node, and later exploits clustering to determine optimally the choice of substitute. Experimental results show the efficiency of the scoring method and the gain obtained by looking for the substitutes in the same cluster and then by the research for the nearest substitutes.

AIP Conference Proceedings, 2008

The process of segmenting images is one of the most critical ones in automatic image analysis who... more The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. Artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the 3 rd generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neuralnetworks. In this paper, we present how SNN can be applied with efficacy in image segmentation.

Communications in Computer and Information Science, 2013

Neural network have seen an explosion of interest over the last years and have been successfully ... more Neural network have seen an explosion of interest over the last years and have been successfully applied across an extraordinary range of problem domains such as medicine, engineering, geology, physique, biology and especially image processing field. In image processing domain, the noise reduction is a very important task. Indeed, many approaches and methods have been developed and proposed in the literature. In this paper, we present a new restoration method for noisy images by minimizing the Total Variation (TV) under constraints using a multilayer neural network (MLP). The proposed method can restore degraded images and preserves the discontinuities. Effectiveness of our proposed approach is showed through the obtained results on different noisy images.

Asian Journal of Applied Sciences, 2011

Journal of Computers, 2015

This paper is dedicated to the presentation of a Radial basis function neural network (RBFNN) bas... more This paper is dedicated to the presentation of a Radial basis function neural network (RBFNN) based denoising method for medical images. In the proposed approach, a RBFNN filter is designed where the output of the network is a single denoised pixel and the inputs are its neighborhood in the degraded image. The back-propagation algorithm is used to train the RBFNN filter by minimizing an appropriate error function obtained from the total variation model. The parameters to be adjusted are the weights and the neurons centers of the RBFNN. The considered filter was used to reduce noise from X-ray, MRI and Mammographic medical images giving good results of noise removal when compared to other approaches and using different noise standard deviations.