Stationary Wavelet Transform(SWT) Based MRI Images Enhancement and Brain Tumor Segmentation
Keywords:
Stationary Wavelet Transform, Adaptive K-means Clustering, BRATSAbstract
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.
References
. Bhatt, M.B., Arya, D., Mishra, A. N., Singh, M., Singh, P., & Gautam, M., “A New Wavelet-based Multifocus Image Fusion Technique using Method Noise-Median Filtering”, In 2019 4th International conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), 2019, pp.1-6, IEEE.
. Nandeesh, M.D., & Meenakshi, M., “A novel technique of medical image fusion using stationary wavelet transform and principal component analysis”, in 2015 international conference on smart sensors and systems (IC-SSS),2015,pp.1-5, IEEE.
. El Mansouri, O., Basarab, A., Vidal, F., Kouame, D., & Tourneret, J.Y., “Fusion of magnetic resonance and ultrasound images: A preliminary study on simulated dat”, in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019, pp.1733-1736, IEEE.
. Min, A., & Kyu, Z. M., “Mri images Enhancement and Tumor Segmentation for Brain”, in 2017 18th Internation Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2017, pp. 170-275, IEEE.
. Jemimma, T. A., & Vetharaj, Y. J., “Watershed Algorithm based DAPP features fro Brain Tumor Segmentation and Classification”, in 2018 Internal Conference on Smart Systems and Inventive Technology (ICSSIT), 2018, pp.155-158, IEEE.
. Padlia, M., & Sharma, J., “Brain tumor segmentation from MRI using fractional sobel mask and watershed transform”, in 2017 International Conference on Infromation Communication, Instrumentation and Control (ICICIC), 2017, pp.1-6, IEEE.
. Ryalat, M. H., Emmens, D., Hulse, M., Bell, D., Al-Rahamneh, Z., Laycock, S., & Fisher, M. (2016, September). Evaluation of particle swarm optimisation for medical image segmentation. In International Conference on Systems Science (pp. 61-72). Springer, Cham.
. Pravallika, K., Lokeswari, G.V., Sowmya G.V., Reddy V.P.C., “An Effective Brain Tumor Segmentation Using K-means clustering”, in International Research Journal of Enginnering and Technology (IRJER), Vol. 05 Mar. 2018.
. Maier, O., Wilms, M., & Handels, H., “Highly discriminative features for glioma segmentation in MR volumes with random forests”, Proccedings of the Multimodal Brain Tumor Image Segmentation Challenge (MICCAI-BRATS), pp.38-41.
. Pereira, S., Pinto, A., Alves, V., & Silva, C.A., “Deep convolutional neural networks for the segmentation of gliomas in mulit-sequence MRI”, IN BrainLes 2015, pp.131-143. Springer, Cham.
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.
