A Survey on Various Edge Detection Techniques in Image Processing and Applied Disease Detection

Wan Muhammad Rahimi Wan Fadzli; Ahmad Yusri Dak; Tajul Rosli Razak

doi:10.24191/jcrinn.v9i2.415

A Survey on Various Edge Detection Techniques in Image Processing and Applied Disease Detection

Authors

Wan Muhammad Rahimi Wan Fadzli College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perlis Branch, Arau Campus, Arau, Perlis. MALAYSIA
Ahmad Yusri Dak College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perlis Branch, Arau Campus, Arau, Perlis. MALAYSIA
Tajul Rosli Razak College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor. MALAYSIA

DOI:

https://doi.org/10.24191/jcrinn.v9i2.415

Keywords:

Edge detection, Image Processing, Gradient-based, Edge detector, Canny

Abstract

This paper surveys various edge detection techniques in image processing, focusing on their applicability to disease detection. Many researchers encompass studies conducted in the context of various crops and fruits, shedding light on their effectiveness and adaptability. However, the more techniques are used and improved, less comparison has been made between them to look further at their challenges, such as noise sensitivity, scale variability, edge linking, and real-world variability. Also, the study will systematically survey and analyze literature on the ability of edge detection, including classical methods like Robert, Sobel, Prewitt, and Canny, as well as more advanced techniques such as gradient-based and Gaussian-based. This research aims to comprehensively understand the strengths and limitations of different edge detection techniques and can be used as a reference point for selecting and enhancing novel techniques in image processing. Overview, this paper makes a substantial contribution to the field by addressing both traditional edge detection in image processing and applied disease detection. It serves as a comprehensive guide, offering insights, practical advice, and a consolidated view of current research trends, and highlights the potential of edge detection in contributing to advancements in disease detection methodologies making it a valuable resource for researchers and practitioners.

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