Brain tumour edge detection using an enhanced canny edge detection algorithm
by A. Anand Selvakumar; P. Thangaraju
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 16, No. 2, 2024

Abstract: Medical image processing is one of today's most advanced and rapidly growing fields. Brain tumours, caused by abnormal cell growth in the brain or spinal canal, are a leading cause of death among children with cancer. Radiologists prioritise rapid and accurate diagnosis to address these disorders. Machine learning-based brain tumour classification is essential for accurate diagnosis. Consistent detection of tumour growth relies on MRI brain scan segmentation, which is crucial for diagnosing neurological disorders like brain tumours. Edge detection highlights boundaries in medical images. This study uses an enhanced canny edge detection algorithm to identify brain tumour edges more effectively. This method only evaluates horizontal and vertical data. The canny method with an 8-directional template improves brain tumour MRI edge recognition. Comparison of proposed edge detection approach to conventional methods. MRI image edge detection is central to this research, as finding and segmenting brain tumours is challenging and time-consuming. MRI scans provide detailed brain images, aiding in visualising abnormal structures. This study enhances tumour detection by using an updated canny edge detection algorithm to identify MRI brain scan boundaries more effectively.

Online publication date: Wed, 16-Oct-2024

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