A new quantum edge detection algorithm for medical images

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. The algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. The algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm.

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Artificial Neural Network Based Fast Edge Detection Algorithm for MRI Medical Images

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v7.i1.pp123-130 ◽

2017 ◽

Vol 7 (1) ◽

pp. 123 ◽

Cited By ~ 1

Author(s):

Teddy Surya Gunawan ◽

Iza Zayana Yaacob ◽

Mira Kartiwi ◽

Nanang Ismail ◽

Nor Farahidah Za'bah ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Image Quality ◽

Edge Detection ◽

Medical Image ◽

Medical Images ◽

Detection Algorithm ◽

Edge Detector ◽

Edge Detection Algorithm ◽

Canny Edge

<p>Currently, magnetic resonance imaging (MRI) has been utilized extensively to obtain high contrast medical image due to its safety which can be applied repetitively. Edges are represented as important contour features in the medical image since they are the boundaries where distinct intensity changes or discontinuities occur. Many traditional algorithms have been proposed to detect the edge, such as Canny, Sobel, Prewitt, Roberts, Zerocross, and Laplacian of Gaussian (LoG). Moreover, many researches have shown the potential of using Artificial Neural Network (ANN) for edge detection. Although many algorithms have been conducted on edge detection for medical images, however higher computational cost and subjective image quality could be further improved. Therefore, the objective of this paper is to develop a fast ANN based edge detection algorithm for MRI medical images. First, we developed features based on horizontal, vertical, and diagonal difference. Then, Canny edge detector will be used as the training output. Finally, optimized parameters will be obtained, including number of hidden layers and output threshold. Results showed that the proposed algorithm provided better image quality while it has faster processing time around three times time compared to other traditional algorithms, such as Sobel and Canny edge detector.</p>

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Medical Images Edge Detection Based on Mathematical Morphology

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2013.1162 ◽

2013 ◽

pp. 7-11

Author(s):

J. Mehena

Keyword(s):

Edge Detection ◽

Medical Image ◽

Medical Images ◽

Detection Algorithm ◽

Medical Image Segmentation ◽

Edge Detection Algorithm ◽

Detection Techniques ◽

Detection Algorithms ◽

Image Edge Detection ◽

Image Edge

Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and reconstruction. Conventionally, edge is detected according to gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of medical images with salt-and-pepper noise. The simulation results shows that the novel mathematical morphological edge detection algorithm is more efficient for image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms. It has been observed that the proposed morphological edge detection algorithm performs better than sobel, prewitt, roberts and canny’s edge detection algorithm. In this paper the comparative analysis of various image edge detection techniques is presented using MATLAB 8.0 .

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Research and Implementation of Medical Image Edge Detection Algorithm

Tobacco Regulatory Science ◽

10.18001/trs.7.5.1.159 ◽

2021 ◽

Vol 7 (5) ◽

pp. 3866-3877

Author(s):

Luo Fugui ◽

Qin Yunchu ◽

Li Mingzhen

Keyword(s):

Medical Imaging ◽

Edge Detection ◽

Medical Image ◽

Medical Images ◽

Small Neighborhood ◽

Detection Algorithm ◽

Effect Change ◽

Edge Detection Algorithm ◽

Image Edge Detection ◽

Image Edge

Medical imaging has become an important reference for the diagnosis of various diseases, and the role of medical imaging will become more and more important in the future. The interpretation of medical images is of paramount importance. With the continuous development of medical imaging technology, image interpretation has become more and more important. At present, it is directly inferred by doctors that in order to solve problems more effectively and deal with fuzzy data, it is necessary to research and implement an algorithm-based medical image edge detection assistant system. The current mainstream algorithms for edge detection include: Roberts, Sobel, Prewitt, etc. Most of these algorithms construct operators for small neighborhood pixels of the original image. The problem is that the algorithm is relatively sensitive to noise in the image and does not automatically select the appropriate threshold, resulting in a result that is not as expected. This is a disadvantage of current algorithms. The thesis elaborates on the theory and algorithm of image edge detection. At the same time, from the perspective of the original edge detection algorithm, the Canny algorithm is mainly studied, and the optimized MTM algorithm and Ostu algorithm are combined to study and optimize in the filtering denoising research. Finally, the algorithm is implemented in C++ language, which realizes the automatic extraction of the edge function of medical images under noise conditions. The improved algorithm performs an edge replacement effect change complared to the conventional algorithm.

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