Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques

We seek to compare three automated diagnosis systems to detect glioma in brain magnetic resonance images (MRIs). Each glioma diagnosis system is composed of four steps. First, a given particle swarm optimization (PSO) technique is used to segment brain magnetic resonance image (MRI). For instance, brain MRI segmentation is performed either by classical PSO, Darwinian particle swarm optimization (DPSO), or fractional-order DPSO (FODPSO). Second, the directional spectral distribution (DSD) signature of the segmented image is computed. Third, multi-fractals of the computed DSD are estimated by generalized Hurst exponents. Fourth, classification of the obtained multi-fractal features is performed by support vector machine (SVM). The leave-one-out cross-validation method (LOOM) is adopted to assess the classification accuracy of all three glioma detection systems. The experimental results indicate that (1) PSO-DSD-MSA system achieved 98.01% ± 0.07 classification accuracy, 100% sensitivity, and 94.78% ± 0.02 specificity, (2) DPSO-DSD-MSA achieved 98.38% ± 0.01 classification accuracy, 99.5% ± 0.02 sensitivity, and 96.70% ± 0.02 specificity, and (3) the FODPSO-DSD-MSA system reached 99.18% ± 0.01 classification accuracy, 100% sensitivity, and 97.95% ± 0.036 specificity. The overall processing time is less than 5 s. All presented systems achieved higher performances in comparison with previous works. As FODPSO-DSD-MSA accuracy is high and processing time is low, it may be a promising automated glioma diagnosis system in clinical milieu.