Three Step Authentication of Brain Tumour Segmentation Using Hybrid Active Contour Model and Discrete Wavelet Transform
Article Sidebar
Main Article Content
Abstract
An innovative imaging research is expected in the medical field due to the challenges and inaccuracies in diagnosing the life-threatened harmful tumours. Brain tumor diagnosis is one of the most difficult areas of study in diagnostic imaging, with the maximum fine for a small glitch given the patients survival rate. Conventionally, biopsy method is used to identify the tumour tissues from the brain's soft tissues by the medical researchers (or) practitioners and it is unproductive due to: (i) it requires more time, and (ii) it may have errors. This paper presents the three-stage authentication-based hybrid brain tumour segmentation process and it makes the detection more accrual. Primarily, tumour area is segmented from a magnetic resonance image and after that when comparing a differentiated segment of an image to the actual image, an improved active contour model is employed to achieve a good match. In addition, discrete wavelet transform is used for the features extraction which leads to improve the accuracy and robustness in the tumour diagnosis. Finally, RELM classifier is used for precise classification of brain tumours. The most effective section of our method is checking the status of the tumour through finding the tumour region. The results are evaluated through new dataset, and it demonstrates that the suggested approach is more efficient than the alternatives as well as provides 96.25% accuracy.
Article Details
References
Global Cancer Observatory. https://gco.iarc.fr/. Accessed 08 Nov 2021.
Cancer statistics, 2020—Siegel—2020. CA Cancer J Clin. Wiley Online Library. https://doi.org/10.3322/caac.21590.
Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies |Neuro-Oncology Oxford Academic. https://academic.oup.com/neuro-oncology/article/15/5/515/1012031. Accessed 08 Nov 2021.
Field M, Witham TF, Flickinger JC, Kondziolka D, Lunsford LD. Comprehensive assessment of hemorrhage risks and outcomes after stereotactic brain biopsy. J Neurosurg. 2001;94(4):545–51. https://doi.org/10.3171/jns.2001.94.4.0545.
Zhou M, et al. Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. Am J Neuroradiol. 2018;39(2):208–16. https://doi.org/10.3174/ajnr.A5391.
Clinical evaluation of a fully-automatic segmentation method for longitudinal brain tumor volumetry Scientific Reports. https://www.nature.com/ articles/srep23376/. Accessed 08 Nov 2021.
Wesseling P, van den Bent M, Perry A. Oligodendroglioma: pathology, molecular mechanisms and markers. Acta Neuropathol. 2015;129(6):809. https://doi.org/10.1007/s00401-015-1424-1.
S. Bauer, R. Wiest, L. P. Nolte, and M. Reyes, “A Survey of MRI-Based Medical Image Analysis for Brain Tumor Studies,” Phys. Med. Biol., vol. 58, no. 13, pp. R97–R129, 2013.
T. D. Pham, “Image Segmentation using Probabilistic Fuzzy C-Means Clustering,” Proc. IEEE Int. Conf Image Process., pp. 722–725, 2001.
P. Anbeek, K. Vincken, and M. Viergever, “Automated MS-Lesion Segmentation by K-Nearest Neighbor Classification,” MIDAS J. - MS Lesion Segmentation (MICCAI 2008 Work., pp. 1–8, 2008.
C. Liu and H. Wechsler, “Gabor Feature-based Classification using the Enhanced Fisher Linear Discriminant Model for Face Recognition,” IEEE Trans. Image Process., vol. 11, no. 4, pp. 467–476, 2002.
M. Kociolek, A. Materka, M. Strzelecki, and P. Szczypinski, “Discrete Wavelet Transform – Derived Features for Digital Image Texture Analysis,” Int. Conf. Signals Electron. Syst., pp. 163–168, 2001.
R. Pourreza, Y. Zhuge, H. Ning, and R. Miller, “Brain Tumor Segmentation in MRI Scan Using Deeply-Supervised Neural Networks,” brainless, Lect. Notes Comput. Sci., vol. 10670, pp. 320–331, 2018.
Y. Li, F. Jia, and J. Qin, “Brain Tumor Segmentation from Multimodal Magnetic Resonance Images via Sparse Representation,” Artif. Intell. Med., vol. 73, pp. 1–13, 2016.
A. Ahmadvand, M. R. Daliri, and S. M. Zahiri, “Segmentation of Brain MR Images using a Proper Combination of DCS based method with MRF,” Multimedia. Tools Appl., vol. 77, no. 7, pp. 8001–8018, 2018.
G. Wang et al., “Interactive Medical Image Segmentation Using Deep Learning with Image-Specific Fine Tuning,” IEEE Trans. Med. Imaging, vol. 37, no. 7, pp. 1562–1573, 2018.
J. Amin, M. Sharif, M. Yasmin, and S. L. Fernandes, “A Distinctive Approach in Brain Tumor Detection and Classification using MRI,” Pattern Recognit. Lett., pp. 1–10, 2017.
J. Cheng, ‘‘Brain tumor dataset (version 5),’’ 2017. doi: 10.6084/m9.figshare.1512427.v5.
J. Cheng et al., ‘‘Enhanced performance of brain tumor classification via tumor region augmentation and partition,’’ PLoS One, vol. 10, no. 12, 2015, Art. no. e0144479.
S. M. K. Hasan and M. Ahmad, “Two-Step Verification of Brain Tumor Segmentation using Watershed-Matching Algorithm,” Brain Informatics, vol. 5, 2018.
J. Paul, ‘‘Deep learning for brain tumor classification,’’ M.S. thesis, Dept. Comput. Sci., Vanderbilt Univ., Nashville, TN, USA, May 2016.
N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, ‘‘Brain tumor classification using convolutional neural network,’’ in World Congress on Medical Physics and Biomedical Engineering. Singapore: Springer, 2018, pp. 183–189.