3D brain tumor segmentation in MRI images based on a modified PSO technique

Abstract

Three‐dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional‐order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1‐C modality.

A novel threshold score based multiclass segmentation technique for brain magnetic resonance images using adaptive opposition slime mold algorithm

“…while itr < Maxitr ð Þ Evaluate the fitness vector f and sort them in ascending order (i.e., minimization problem) using Equation (14). Extract and update the necessary parameters such as f LB , X LB , f LW , f GB and X GB using Equations (15)(16)(17)(18)(19). Update the oscillation parameter weight W , velocity V b and V c using Equations (20)(21)(22).…”

“…15 The basic version of the optimization method is inadequate for all types of difficulties, prompting the hybridization of algorithms or the modification of the search strategy to get a more effective multilevel thresholding performance on the brain MR images. Some applications in the brain MR image thresholding reported, using the hybrid/ modified optimization algorithms, are as follows: adaptive bacterial foraging (ABF), 16 real coded genetic algorithm (GA) using simulated binary crossover (SBX), 17 mutationbased particle swarm optimization (MPSO), 18 modified particle swarm optimization 19 and adaptive wind-driven optimization (AWDO). 20 These basic/hybrid/modified optimization algorithms are used in the histogram-based multilevel thresholding such as Otsu's methods, 16,18,20 Kapur's entropy, 1,14,16,17,20 Shannon entropy, 14 Tsallis entropy 14 and cross entropy.…”

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

“…Artificial bee colony (ABC) is a swarm intelligence-based evolutionary algorithm that is inspired by exploring the food source behavior of honey bees. Similar to many other metaheuristic algorithms such as differential evolution (DE) algorithm, ant colony (ACO) algorithm, particle swarm optimization (PSO), and gravitational emulation that are adapted to solve many different problems such as routing [15,16], image segmentation [17][18][19][20][21][22], and many other clustering problems [23][24][25], ABC is also used in solving many different problems [26,27]. In the study by Hafez et al…”