Empirical Study of MRI Brain Tumor Edge Detection Algorithms

  • laila alsenawi Information Science Department, Kuwait, Kuwait University
  • Reem AlJeeran Information Science Department, Kuwait, Kuwait University
  • Kalim Qureshi Information Science Department, Kuwait, Kuwait University
Keywords: Brain Tumor, MRI, Edge Detection, Tumor Edge Detection, Canny algorithm, Prewitt algorithm

Abstract

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.

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Published
2022-06-23
How to Cite
alsenawi, laila, Reem AlJeeran, & Kalim Qureshi. (2022). Empirical Study of MRI Brain Tumor Edge Detection Algorithms. International Journal of Computer (IJC), 43(1), 91-100. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1940
Section
Articles