Abstract
Edge detection consists of a set of mathematical methods which identifies the points in a digital image where image brightness changes sharply. In the traditional edge detection methods such as the first-order derivative filters, it is easy to lose image information details and the second-order derivative filters are more sensitive to noise. To overcome these problems, the methods based on the fractional differential-order filters have been proposed in the literature. This paper presents the construction and implementation of the Prewitt fractional differential filter in the Asumu definition sense for SARS-COV2 image edge detection. The experiments show that these filters can avoid noise and detect rich edge details. The experimental comparison show that the proposed method outperforms some edge detection methods. In the next paper, we are planning to improve and combine the proposed filters with artificial intelligence algorithm in order to program a training system for SARS-COV2 image classification with the aim of having a supplemental medical diagnostic.
Highlights
Digital image processing is an open field of research
In the area of image processing, the edges of a digital image can be defined as transitions between two regions of significantly different gray levels
The proposed filters have demonstrated the capability of obtaining different edge detection images by varying the fractional differential order
Summary
Digital image processing is an open field of research. The constant progress in this area has not been by itself, but in conjunction with other areas with which it is related such as mathematics, computing, and the increasing knowledge of certain organs of the human body that intervene in perception and in image manipulation. The development of hardware languages and programming languages, it became possible to use and apply mathematical methods in this area in a wide range of applications such as medicine, biology, archeology, geology, and astronomy. In the area of image processing, the edges of a digital image can be defined as transitions between two regions of significantly different gray levels. Edge detection in an image is extremely important and useful, as they provide valuable information about the boundaries of objects and facilitate many tasks, such as object recognition, and region segmentation, for which a variety of mathematical edge detection algorithms have been developed. In [3], the Sobel edge detection algorithm is used previously to process the image, the edge image is acquired, the radar remote sensing image is analyzed from different angles, and the different radar remote sensing images are transformed. The great inconvenience of this type of technique is that it produces thicker edges in images, and it has poor detection quality
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