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An effective tumor detection approach using denoised MRI based on fuzzy bayesian segmentation approach

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Abstract

In the cancer treatment, the accurate segmentation of the brain tumor is a major task for which the denoised image gains major significance. This paper proposes an effective segmentation of the brain tumor for accurate detection of the brain tumors using the MRI brain image. The tumor is segmented using the proposed fuzzy Bayesian cut brain tumor segmentation approach. Initially, the noise from the MRI images is removed using the image denoising algorithm, known as Taylor-Krill Herd-based SVM algorithm. The denoised MRI output from the filter is then subjected to tumor segmentation using the proposed Fuzzy Bayesian cut brain tumor segmentation approach, which is the inclusion of the fuzzy and Gaussian naive bayes concept in the tumor cut algorithm in order to enable effective segmentation. The experimentation is performed using the BraTS database and simulated BraTS database and the comparative analysis of the proposed Fuzzy Bayesian cut brain tumor segmentation approach is performed with the other state-of-the-art method based on the metrics, such as accuracy, Jaccard similarity, Sensitivity, Specificity, and Peak signal-to-noise ratio (PSNR). The simulation results reveal that the proposed method acquired a maximum accuracy of 0.9903, by considering the Rayleigh noise using simulated BRaTS database, which is 0.20%, 0.33%, and 0.02%, better than the existing methods, such as original tumor cut, wiener filter + fuzzy Bayesian cut algorithm, and median filter + fuzzy Bayesian cut algorithm, respectively.

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Authors and Affiliations

  1. SCOPE, Vellore Institute of Technology, Vellore, Tamilnadu, India

    C. Narasimha & A. Nagaraja Rao

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  1. C. Narasimha
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  2. A. Nagaraja Rao
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Correspondence to A. Nagaraja Rao.

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Narasimha, C., Rao, A.N. An effective tumor detection approach using denoised MRI based on fuzzy bayesian segmentation approach. Int J Speech Technol 24, 259–280 (2021). https://doi.org/10.1007/s10772-020-09782-z

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  • DOI: https://doi.org/10.1007/s10772-020-09782-z

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