Stationary Wavelet Transform(SWT) Based MRI Images Enhancement and Brain Tumor Segmentation

  • Aye Min University of Computer Studies, Mandalay (UCSM), Mandalay, Myanmar
  • Nu War University of Computer Studies, Mandalay (UCSM), Mandalay, Myanmar
Keywords: Stationary Wavelet Transform, Adaptive K-means Clustering, BRATS

Abstract

Brain tumor is the anomalous growing of Brain cancer cells. Because of its complex structure, brain tumor segmentation and identification are very difficult tasks in medical field. As with MR image processing, MR images are particularly sensitive to noise, resulting in errors in image acquisition and transmission such as Gaussian noise and impulse noise, etc. MRI image is filtered with Median filter and Wiener filter simultaneously to improve the MR image The Stationary Wavelet Transform (SWT) is then used to combine both Median and Wiener filter results. After preprocessing, Adaptive K-means clustering is used for image segmentation. In the post processing step, morphological operation and Median filter are used to get better segmentation results. This method is applied to the BRATS-2015 dataset, which consists of multi-sequence MRI data available to the public from patients with brain tumors. The well-known, based line methods are compared for comparing the proposed system. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) are used in evaluation of the enhancement. For testing tumor segmentation measures, True Positive Rate (TPR), True Negative Rate (TNR), Accuracy, and Jaccard Similarity Index are used. Compared with dependent line methods and state of the art, this system performs well, especially for the entire tumor area.

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Published
2020-08-27
How to Cite
Min, A., & War, N. (2020). Stationary Wavelet Transform(SWT) Based MRI Images Enhancement and Brain Tumor Segmentation. International Journal of Computer (IJC), 39(1), 26-35. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1803
Section
Articles