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Computer-aided detection of mesial temporal sclerosis based on hippocampus and cerebrospinal fluid features in MR images

Wang Huiquan, Ahmed S. Nizam, Mandal Mrinal

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 1

122--132

Mesial temporal sclerosis (MTS) is the commonest brain abnormalities in patients with intractable epilepsy. Its diagnosis is usually performed by neuroradiologists based on visual inspection of magnetic resonance imaging (MRI) scans, which is a subjective and time-consuming process with inter-observer variability. In order to expedite the identification of MTS, an automated computer-aided method based on brain MRI characteristics is proposed in this paper. It includes brain segmentation and hippocampus extraction followed by calculating features of both hippocampus and its surrounding cerebrospinal fluid. After that, support vector machines are applied to the generated features to identify patients with MTS from those without MTS. The proposed technique is developed and evaluated on a data set comprising 15 normal controls, 18 left and 18 right MTS patients. Experimental results show that subjects are correctly classified using the proposed classifiers with an accuracy of 0.94 for both left and right MTS detection. Overall, the proposed method could identify MTS in brain MR images and show a promising performance, thus showing its potential clinical utility.

Bat optimization based neuron model of stochastic resonance for the enhancement of MR images

Singh M., Verma A., Sharma N.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 1

124--134

Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images.