Empirical Study of MRI Brain Tumor Edge Detection Algorithms
Keywords:
Brain Tumor, MRI, Edge Detection, Tumor Edge Detection, Canny algorithm, Prewitt algorithmAbstract
A brain tumor refers to the abnormal growth of cells that can be found in the brain or the skull. MRI is a type of advanced medical imaging that provides detailed information about the anatomy of the human soft tissues. Medical experts perform tumor segmentation using magnetic resonance imaging (MRI) data, which is an essential part of cancer diagnosis and treatment. Tumor detection refers to the methods that are used to diagnose cancer or other types of diseases. Edge detection is also one of the common methods that come under the process of treating medical images. The main objective of edge detection is discovering information about the shapes, transmission, and reflection of images. In this paper, we investigated the performance comparison MRI brain tumor edge detection Algorithms. The Canny, and Prewitt are used for investigation. As result, Canny edge detection is better than Prewitt in term of clarity and visibility for the tumor.
References
N. B. Bahadure, A. K. Ray and H. P. Thethi, "Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM," International journal of biomedical imaging, 2017.
R. R. Gharieb, "Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation," Recent Applications in Data Clustering, no. 35, 2018.
J. Jin, Electromagnetic analysis and design in magnetic resonance imaging, Routledge, 2018.
A. H. Abdel-Gawad, L. A. Said and A. G. Radwan, "Optimized edge detection technique for brain tumor detection in MR images," IEEE Access, vol. 8, pp. 136243-136259, 2020.
C. L. Choudhury, C. Mahanty, R. Kumar and K. B. Mishra, "Brain tumor detection and classification using convolutional neural network and deep neural network," in 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), 2020.
Z. Stosic and P. Rutesic, "An improved canny edge detection algorithm for detecting brain tumors in MRI images," International Journal of Signal Processing, p. 3, 2018.
A. Lakshmi, Performance analysis of brain tumor Segmentation and classification in brain MRI using soft computing techniques, Kalasalingam University, 2017.
AANS, "Brain Tumors," American Association of Neurological Surgeons, [Online]. Available: https://www.aans.org/en/Patients/Neurosurgical-Conditions-and-Treatments/Brain-Tumors. [Accessed 30 November 2021].
A. Jayachandran and R. Dhanasekaran, "Brain tumor detection and classification of MR images using texture features and fuzzy SVM classifier," Research Journal of Applied Sciences, Engineering and Technology, vol. 12, no. 6, pp. 2264-2269, 2013.
M. Mukhtar, M. Bilal, A. Rahdar, M. Barani, R. Arshad, T. Behl and S. Bungau, "Nanomaterials for diagnosis and treatment of brain cancer: Recent updates," Chemosensors, vol. 4, no. 8, p. 117, 2020.
L. L. Wald, P. C. McDaniel, T. Witzel, J. P. Stockmann and C. Z. Cooley, "Low‐cost and portable MRI," Journal of Magnetic Resonance Imaging, vol. 3, no. 52, pp. 686-696, 2020.
S. Sahir, "Canny Edge Detection Step by Step in Python - Computer Vision," Towards Data Science, 25 January 2019. [Online]. Available: https://towardsdatascience.com/canny-edge-detection-step-by-step-in-python-computer-vision-b49c3a2d8123. [Accessed 25 December 2021].
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