Paper Title : Spatial Domain Segmentation Algorithm for Tumour Detection and Wavelet Based Texture Analysis

ISSN : 2394-2231
Year of Publication : 2021

10.29126/23942231/IJCT-v8i2p38

Authors: Shawni Dutta , Prof. Samir Kumar Bandyopadhyay

         



MLA Style: Shawni Dutta , Prof. Samir Kumar Bandyopadhyay " Spatial Domain Segmentation Algorithm for Tumour Detection and Wavelet Based Texture Analysis " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Shawni Dutta , Prof. Samir Kumar Bandyopadhyay " Spatial Domain Segmentation Algorithm for Tumour Detection and Wavelet Based Texture Analysis " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
The most challenging and complex area of research in biomedical image processing is segmentation and analysis of brain tumour. It is proved by Statistics that amongst various brain ailments, brain tumour is may be fatal if it will be carcinogenic. The paper proposes a spatial domain segmentation algorithm for detection of brain tumour using multiple images of brain MR and k-means algorithm. Also a Brain Tumour Texture Analysis algorithm is proposed that uses fractal dimension, fractal area, and wavelet to classify type of texture present in brain tumour. The results obtained by those algorithms are found to be highly satisfactory and verified for ground truth by medical practitioners. The proposed algorithms were compared with other stateof-the-art algorithms and found to be better in terms of accuracy, precision and recall.

Reference
1. K. Sinha and G. R. Sinha, “Efficient segmentation methods for tumor detection in MRI images,” in Proceedings of the IEEE Students’ Conference on Electrical, Electronics and Computer Science, pp. 1–6, IEEE, Piscataway, NJ, USA, 2014. 2. Y. Megersa and G. Alemu, “Brain tumor detection and segmentation using hybrid intelligent algorithms,” in Proceedings of the AFRICON, pp. 1–8, IEEE, Piscataway, NJ, USA, 2015. 3. A. Hazra, A. Dey, S. K. Gupta, and M. A. Ansari, “Brain tumor detection based on segmentation using MATLAB,” in Proceedings of the International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 425–430, IEEE, Piscataway, NJ, USA, 2017. 4. A. Hanuman and K. Sooknanan, “Brain tumor segmentation and volume estimation from T1-contrasted and T2 MRIs,” International Journal of Image Processing (IJIP), vol. 12, no. 2, pp. 48–62, 2018. 5. C. Zhang, X. Shen, H. Cheng, and Q. Qian, “Brain tumor segmentation based on hybrid clustering and morphological operations,” International Journal of Biomedical Imaging, vol. 2019, Article ID 7305832, 11 pages, 2019. 6. P. Qian, H. Friel, M. S. Traughber et al., “Transforming UTE-mDixon MR abdomenpelvis images into CT by jointly leveraging prior knowledge and partial supervision,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020. 7. P. Qian, Y. Chen, J.-W. Kuo et al., “mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification,” IEEE Transactions on Medical Imaging, vol. 39, no. 4, pp. 819–832, 2020. 8. Y. Jiang, K. Zhao, K. Xia et al., “A novel distributed multitask fuzzy clustering algorithm for automatic MR brain image segmentation,” Journal of Medical Systems, vol. 43, no. 5, pp. 118:1–118:9, 2019. 9. Z. Zhang and E. Sejdić, “Radiological images and machine learning: trends, perspectives, and prospects,” Computers in Biology and Medicine, vol. 108, no. 6, pp. 354– 370, 2019. 10. Wang S, Meng M, Zhang X, Wu C, Wang R, Wu J, et al. Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest. Oncol Lett. 2018;15(5):7297–7304. pmid:29731887. 11. Mohan G, Subashini MM. MRI based medical image analysis: Survey on brain tumor grade classification. Biomed Signal Process Control. 2018;39: 139–161. 12. Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDGPET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60(14):5471

Keywords
—— MRI, Texture Analysis, Brain Tumour, Segmentation and Wavelet Transform