No abstract

Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan–Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.

In this paper, we propose a parallel algorithm for data classification, and its application for Magnetic Resonance Images (MRI) segmentation. The studied classification method is the well-known c-means method. The use of the parallel architecture in the classification domain is introduced in order to improve the complexities of the corresponding algorithms, so that they will be considered as a pre-processing procedure. The proposed algorithm is assigned to be implemented on a parallel machine, which is the reconfigurable mesh computer (RMC). The image of size (m  n) to be processed must be stored on the RMC of the same size, one pixel per processing element (PE).

Proposing an efficient strategy to reduce traffic congestion is an essential step towards improvement as we take into consideration the unpredictable and dynamic infrastructure of the road network. With the advances in computing technologies and communications protocols, we can retrieve any type of data and receive in real-time the state of traffic congestion at each road using Electronic Toll Collection System (ETCS), Vehicle Traffic Routing System (VTRS), Intelligent Transportation System (ITS) and Traffic Light Signals (TLS). This study introduces a new distributed strategy that aims to optimize traffic road congestion in realtime based on the Vehicular Ad-Hoc Network (VANET) communication system and the techniques of the Ant Colony Optimization (ACO). The VANET is used as a communication technology that will help us create a channel of communication between several vehicles and routes. The techniques of the ACO is used to compute the shortest path that can be followed by the driver to avoid congested routes. The proposed system is based on a multi-agent architecture, in which all agents work together to monitor the road traffic congestion and help drivers quickly arrive at their destinations by following the best routes with less congestion. Simulation results show that the proposed method can reduce the total distance traveled and time taken in order to reach a destination, as compared to the classic "shortest path method" (based only on the distance).