Fayçal Hamdaoui

A New Images Segmentation Method Based on Modified Particle Swarm Optimization Algorithm

The partitioning of an image into several constituent components is called image segmentation. Many approaches have been developed; one of them is the particle swarm optimization (PSO) algorithm, which is widely used. PSO algorithm is one of the most recent stochastic optimization strategies. In this article, a new efficient technique for the magnetic resonance imaging (MRI) brain images segmentation thematic based on PSO is proposed. The proposed algorithm presents an improved variant of PSO, which is particularly designed for optimal segmentation and it is called modified particle swarm optimization. The fitness function is used to evaluate all the particle swarm in order to arrange them in a descending order. The algorithm is evaluated by performance measures such as run time execution and the quality of the image after segmentation. The performance of the segmentation process is demonstrated by using a defined set of benchmark images and compared against conventional PSO, genetic algorithm, and PSO with Mahalanobis distance based segmentation methods. Then we applied our method on MRI brain image to determinate normal and pathological tissues.

Fast MR brain image segmentation based on modified Shuffled Frog Leaping Algorithm

Due to the need of correct diseases analysis, MR image segmentation remains till now a challenging problem, especially in the presence of random noise. This paper proposes a new meta-heuristic algorithm for MR brain image segmentation, named Modified Shuffled Frog Leaping Algorithm (MSFLA), based on the technique of Shuffled Frog Leaping Algorithm (SFLA). In this new paradigm, there is no need to filter the original image. The new fitness function proposed in our algorithm helps to evaluate quickly the particle frogs in order to arrange them in descending order. The proposed approach has been compared with other meta-heuristics such as 3D-Otsu thresholding with SFLA and Genetic Algorithm (GA) and also with the algorithm of segmentation using the Rician Classifier (RiCE). Experimental results show that the proposed MSFLA is able to achieve better segmentation quality and execution time than the latest methods.

An Efficient Multi Level Thresholding Method for Image Segmentation Based on the Hybridization of Modified PSO and Otsu’s Method

In the area of image processing, segmentation of an image into multiple regions is very important for classification and recognition steps. It has been widely used in many application fields such as medical image analysis to characterize and detect anatomical structures, robotics features extraction for mobile robot localization and detection and map procession for lines and legends finding. Many techniques have been developed in the field of image segmentation. Methods based on intelligent techniques are the most used such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) called metaheuristics algorithms. In this paper, we describe a novel method for segmentation of images based on one of the most popular and efficient metaheuristic algorithm called Particle Swarm optimization (PSO) for determining multilevel threshold for a given image. The proposed method takes advantage of the characteristics of the particle swarm optimization and improves the objective function value to updating the velocity and the position of particles. This method is compared to the basic PSO method, also, it is compared with other known multilevel segmentation methods to demonstrate its efficiency. Experimental results show that this method can reliably segment and give threshold values than other methods considering different measures.

FPGA hardware architecture of correlation-based MRI images classification using XSG

Medical imaging classification is one of the areas where using algorithm-based hardware architecture improves performance, in terms of time processing. It gives better and clearer results than when using software implementation. Today, advantages of field-programmable gate array FPGA, including reusability, filed reprogramability, simpler design cycle, fast marketing and a combination of the main advantages of ASICs and DSPs make them powerful and very attractive devices for rapid prototyping of all images processing applications. In this paper, we use Xilinx system generator XSG environment to develop a hardware classification-based correlation algorithm from a system level approach. This architecture may be of great influence on the final choice to prove if the MRI image is with lesions brain or normal. Results are illustrated on a simple example for brain magnetic resonance imaging MRI images classification. Two sets are used: a set of normal MR images and another set with MR lesion brain images.

FPGA implementation of particle swarm optimization based on new fitness function for MRI images segmentation

Magnetic resonance imaging (MRI) is considered as a key part in therapeutic procedures because it clearly defines the aim. It also avoids sensitive organs and it determines the desired paths. This phenomenon requires image processing operations such as segmentation to locate the tumor. Medical image segmentation is still an important topic in the field of brain tumor. In the present article, we propose a Hardware Architecture of segmentation based on a Modified Particle Swarm Optimization (HAMPSO) algorithm for MRI images segmentation. To achieve this, we use the Xilinx System Generator (XSG) to be implemented on a Field Programmable Gate Array (FPGA). This architecture is based on a new variant of objective function. These performances of the proposed method are proved using a set of MRI images and were compared to the Hardware Architecture of segmentation based on Particle Swarm Optimization (HAPSO) in terms of either device utilization, execution time, qualitatively or quantitatively results.

Comparison between MPSO and MSFLA metaheuristics for MR brain image segmentation

This paper presents a comparison study between two metaheuristics swarm intelligence (SI) techniques based Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA), to solve images segmentation problems. Performances in terms of Threshold values and run time execution of both Modified PSO (MPSO) and Modified SFLA (MSFLA) algorithms are reviewed and checked through MR brain medical images application that consist of partitioning an image into two regions, so get a binary image. MPSO and MSFLA are based on a new fitness function, which justifies their appointment.

Hardware architecture: Correlation-based approach for road sign detection

Road Sign Detection (RSD) is becoming a major goal of the safety Advanced Driving Assistance Systems (ADAS). Automotive research area share many publications based various techniques used to detect and classify signs. This paper provides a hardware detection-based correlation architecture using Xilinx System Generator (XSG). This proposed architecture outsets with pre-processing step: RGB to YCrCb space, thresholding and closing (erosion and then dilation). Then signs are classified using an intelligent technique: the correlation method. Experimental results are demonstrated using the proposed architecture applied on a set of traffic signs stored in a database, and then compared to software results to conclude about the the quality and the efficiencies of this architecture.

Multi-level fractional order PSO new paradigm algorithm for image segmentation

Nowadays, the resolution of the image characterisation problem is a very active and developed research field. Various applications areas were processed such as robotics and autonomous systems, computer vision, map processing and medical imaging. Pre-processing, segmentation, recognition and classification are the main areas of study. Multilevel segmentation of Benchmark images is our application field in this study. It is used to separate the original image into regions with common characteristics of an interesting viewing quality and a fast time execution. The purpose is to automatically determine the optimal threshold values based between-class variance maximisation. The choice of the already used method essentially depends on the quantitative characteristics. We note the optimal threshold values, the minimum CPU processing time and the stability of the proposed fitness function. Likewise, qualitatively results are required. The principle aim of this paper is to propose a new PSO algorithm for multilevel segmentation based on a novel fitness function and modified inertia component to find the optimal thresholds. Experimental results applied on a set of benchmarks images have been proven efficiencies and advantages in multilevel compared to other metaheuristics such as Genetic Algorithms (GA), Otsu method, Conventional PSO and Fractional-Order Darwinian PSO (DPSO).