A Systematic Literature Review: Performance Comparison of Edge Detection Operators in Medical Images

Abstract

Medical images play a crucial role in the diagnosis of diseases. To make the diagnosis more accurate, the image should usually be enhanced first using image processing methods such as segmentation and edge detection stages. However, the complexity and noise that may arise in these images pose challenges in edge detection. Therefore, to portray the characteristics of edge detection operators, this research presents a systematic literature review of the performance of various edge detection operators in medical images, focusing on literature published between 2019 and 2023. After the selection process, 41 papers out of the initial 112 collected papers were chosen for further review. The study evaluates edge detection operators e.g., Canny, Sobel, Prewitt, Roberts, and Laplacian of Gaussian (LOG) on medical images such as X-rays, MRI, CT scans, ultrasound, Pap smears, and others. In the analysis, the accuracy, computational time, and response to noise of each operator are compared. The results indicate that despite longer computational times, Canny emerges as the most accurate operator, especially in Pap smear and CT scan images. The LOG operator offers high accuracy in MRI images with more efficient computational time. Evaluation of operator reliability against noise confirms the superiority of Canny. Furthermore, the future potential of edge detection in medical services is also reviewed. For instance, Canny, known for accurate and noise-resistant edges, enhances detection in complex CT-Scan and X-ray images. Meanwhile, LOG, handling artifacts with lower computational time, improves edge clarity in medical images. Potential applications include enhanced diagnosis, efficient patient monitoring, and improved image clarity in future medical services.