The Algorithm of Image Edge Detection on Panoramic Dental X-Ray using Multiple Morphological Gradient (mMG) Method

2016 ◽  
Vol 6 (6) ◽  
pp. 1012 ◽  
Author(s):  
Jufriadif Na`am ◽  
Johan Harlan ◽  
Sarifuddin Madenda ◽  
Eri Prasetio Wibowo
Keyword(s):  
Edge Detection ◽  
X Ray ◽  
Morphological Gradient ◽  
Image Edge Detection ◽  
Image Edge

scholarly journals Enhancing Ant-Based Algorithms for Medical Image Edge Detection by Admissible Perturbations of Demicontractive Mappings

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 885
Author(s):  
Vasile Berinde ◽  
Cristina Ţicală
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Test Functions ◽  
Head Ct ◽  
X Ray ◽  
Numerical Tests ◽  
Image Edge Detection ◽  
Evaluation Of Parameters ◽  
Image Edge ◽  
Demicontractive Mappings

The aim of this paper is to show analytically and empirically how ant-based algorithms for medical image edge detection can be enhanced by using an admissible perturbation of demicontractive operators. We thus complement the results reported in a recent paper by the second author and her collaborators, where they used admissible perturbations of demicontractive mappings as test functions. To illustrate this fact, we first consider some typical properties of demicontractive mappings and of their admissible perturbations and then present some appropriate numerical tests to illustrate the improvement brought by the admissible perturbations of demicontractive mappings when they are taken as test functions in ant-based algorithms for medical image edge detection. The edge detection process reported in our study considers both symmetric (Head CT and Brain CT) and asymmetric (Hand X-ray) medical images. The performance of the algorithm was tested visually with various images and empirically with evaluation of parameters.

Edge Detection of Color Images Based on Improved Morphological Gradient Operators

2014 ◽  
Vol 511-512 ◽  
pp. 550-553 ◽  
Author(s):  
Jian Yong Liang
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Color Image ◽  
Color Images ◽  
Morphological Gradient ◽  
Image Edge Detection ◽  
Gradient Based ◽  
Image Edge ◽  
Edge Detection Method ◽  
Traditional Approaches

Edge detection is an old and hot topic in image processing, pattern recognition and computer vision. Numerous edge detection approaches have been proposed to gray images. It is difficult to extend these approaches to color image edge detection. A novel edge detection method based on mathematical morphology for color images is proposed in this paper. The proposed approach firstly compute vector gradient based on morphological gradient operators, and then compute the optimal gradient according to structure elements with different size. Finally, we use a threshold to binary the gradient images and then obtain the edge images. Experimental results show that the proposed approach has advantages of suppressing noise and preserving edge details and it is not sensitive to noise pixel. The finally edge images via the proposed method have high PSNR and NC compared with the traditional approaches.

Spline Interpolation Sub Pixel Edge Detection Method Based on Improved Morphological Gradient

2014 ◽  
Vol 971-973 ◽  
pp. 1756-1759 ◽  
Author(s):  
Chun Yan Nan ◽  
Xiao Hui Yang
Keyword(s):  
Edge Detection ◽  
Simulation Experiment ◽  
Detection Method ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Morphological Gradient ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method ◽  
Improved Methods

In order to improve the accuracy of image edge detection.A spline interpolation sub pixel edge detection method based on improved morphological gradient is proposed in the thesis.Firstly,using improved morphological gradient filter operator for image coarse positioning;Then,the cubic spline interpolation method is carried out for pixel-level edge of the image interpolation so that the image edge locates in sub-pixel level.Have a simulation experiment to improved methods by Matlab, results show that the improved method can accurately detect the edge of the image, edge detection is fine, the precision of positioning is hight and the result of detection is good.

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