Optimized Shannon and Fuzzy Entropy based Machine Learning Model for Brain MRI Image Segmentation

543-549The pre-processing procedures for medical image segmentation are a crucial task in MRI image study. The medical image thresholding approaches are competent for bi level thresholding due to its' easiness, strength, fewer convergence period and accurateness. The efficiency can be maintained using an extensive search which can be employed for choosing the best thresholds. In this scenario, swarm intelligence-based learning algorithms can be suitable to gain the best thresholds. In this paper, we have focused in thresholding algorithm for segmentation of MRI brain image by maximizing fuzzy entropy and Shannon Entropy using machine learning and new evolutionary techniques. We have considered, Whale Optimization algorithm (WOA) in order to find the best outcome as well as compared the obtained results with the Shannon Entropy or fuzzy entropy-based examination that are fundamentally improved by Differential Evolution (DE), Particle Swarm Optimization (PSO), Social group optimization algorithm (SGO). It is discovered that overall operation could be effective by the strategy in features which can be captured through picture similarity matrix along with entropy values. We have observed that the proposed whale optimization model is able to better optimize the Shannon and fuzzy entropy compared to other swarm intelligence algorithms. It is also noticed that the new swarm intelligent algorithm i.e Social Group Optimization algorithm (SGO) is also performing better than the other two optimization algorithms i.e., Differential Evolution (DE), Particle Swarm Optimization (PSO) and providing very closer performance compared to Whale optimization algorithm. However, social group optimization algorithm requires little less CPU time than whale optimization algorithm

Optimized Shannon and Fuzzy Entropy based Machine Learning Model for Brain MRI Image Segmentation

The pre-processing procedures for medical image segmentation are a crucial task in MRI image study. The medical image thresholding approaches are competent for bi level thresholding due to its' easiness, strength, fewer convergence period and accurateness. The efficiency can be maintained using an extensive search which can be employed for choosing the best thresholds. In this scenario, swarm intelligence-based learning algorithms can be suitable to gain the best thresholds. In this paper, we have focused in thresholding algorithm for segmentation of MRI brain image by maximizing fuzzy entropy and Shannon Entropy using machine learning and new evolutionary techniques. We have considered, Whale Optimization algorithm (WOA) in order to find the best outcome as well as compared the obtained results with the Shannon Entropy or fuzzy entropy-based examination that are fundamentally improved by Differential Evolution (DE), Particle Swarm Optimization (PSO), Social group optimization algorithm (SGO). It is discovered that overall operation could be effective by the strategy in features which can be captured through picture similarity matrix along with entropy values. We have observed that the proposed whale optimization model is able to better optimize the Shannon and fuzzy entropy compared to other swarm intelligence algorithms. It is also noticed that the new swarm intelligent algorithm i.e Social Group Optimization algorithm (SGO) is also performing better than the other two optimization algorithms i.e., Differential Evolution (DE), Particle Swarm Optimization (PSO) and providing very closer performance compared to Whale optimization algorithm. However, social group optimization algorithm requires little less CPU time than whale optimization algorithm

Cross‑Modal Reaction of Auditory and Visual Cortices After Long‑Term Bilateral Hearing Deprivation in the Rat

[Abstract] Visual cortex (VC) over-activation analysed by evoked responses has been demonstrated in congenital deafness and after longterm acquired hearing loss in humans. However, permanent hearing deprivation has not yet been explored in animal models. Thus, the present study aimed to examine functional and molecular changes underlying the visual and auditory cross-modal reaction. For such purpose, we analysed cortical visual evoked potentials (VEPs) and the gene expression (RT-qPCR) of a set of markers for neuronal activation (c-Fos) and activity-dependent homeostatic compensation (Arc/Arg3.1). To determine the state of excitation and inhibition, we performed RT-qPCR and quantitative immunocytochemistry for excitatory (receptor subunits GluA2/3) and inhibitory (GABAA-α1, GABAB-R2, GAD65/67 and parvalbumin-PV) markers. VC over-activation was demonstrated by a signifcant increase in VEPs wave N1 and by up-regulation of the activity-dependent early genes c-Fos and Arc/Arg3.1 (thus confrming, by RT-qPCR, our previously published immunocytochemical results). GluA2 gene and protein expression were signifcantly increased in the auditory cortex (AC), particularly in layers 2/3 pyramidal neurons, but inhibitory markers (GAD65/67 and PV-GABA interneurons) were also signifcantly upregulated in the AC, indicating a concurrent increase in inhibition. Therefore, after permanent hearing loss in the rat, the VC is not only over-activated but also potentially balanced by homeostatic regulation, while excitatory and inhibitory markers remain imbalanced in the AC, most likely resulting from changes in horizontal intermodal regulationMinisterio de Economía y Competitividad; SAF2016–78898-C2-2-RMinisterio de Economía y Competitividad; BFU2017-82375-RJunta de Castilla y León; SA070P1