An Optimized Cascaded Stochastic Resonance for the Enhancement ofBrain MRI

Singh, Munendra and Verma, V and Sharma, N (2018) An Optimized Cascaded Stochastic Resonance for the Enhancement ofBrain MRI. IRBM, 39 (5). pp. 334-342. ISSN 1959-0318

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Background: The contrast enhancement of Magnetic Resonance Imaging (MRI) data is quite challenging as the noise present in this data also get amplified in this process. Dynamic Stochastic Resonance (DSR) is the technique that utilizes the noise to enhance the contrast of MRI data. Method: The present study proposes the cascaded stochastic resonance, which exploits the properties of modified potential neuron model and quartic bistable model of DSR. The Multi-objective Particle Swarm Optimization (MOPSO) tunes the DSR parameters associated with the cascading of both the models. The MOPSO produces a set of the solution called Pareto front for the maximization of two image quality measures, i.e., contrast enhancement factor and universal image quality index. Further, the maximization of another image quality measure, i.e., anisotropy helps to obtain the optimum enhanced image from the Pareto fronts solution. Results: The present study included the simulated and real MRI data. The results show that the proposed method obtained mean contrast enhancement factor, universal image quality index and anisotropy equal to 1.79, 0.78 and 0.021 respectively. These values are better than those obtained for classical bistable DSR and other conventional contrast enhancement techniques. The proposed algorithm has been tested on real MRI data as well and found valuable in the diagnosis of lacunar infarct and mesial temporal sclerosis. Conclusion: The cascaded DSR based on MOPSO has shown promising results and may be highly beneficial to the diagnosis of different brain pathology. ©2018

Item Type: Article
Uncontrolled Keywords: Cascaded stochastic resonance, Particle swarm optimization, Contrast enhancement, Brain, MRI
Subjects: Engineering > MIT Manipal > Mechatronics
Depositing User: MIT Library
Date Deposited: 04 Feb 2019 08:41
Last Modified: 04 Feb 2019 08:41

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