Medical Image Edge Detection Based on Soft Computing Approach

2015 ◽  
Vol 03 (07) ◽  
pp. 6801-6807 ◽  
Author(s):  
J.Mehena, M. C. Adhikary
Keyword(s):  
Edge Detection ◽  
Soft Computing ◽  
Medical Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Computing Approach

scholarly journals Image Edge Detection based on Soft Computing Approach

2012 ◽  
Vol 51 (8) ◽  
pp. 12-14 ◽  
Author(s):  
Pushpajit A.Khaire ◽  
Nileshsingh V. Thakur
Keyword(s):  
Edge Detection ◽  
Soft Computing ◽  
Image Edge Detection ◽  
Image Edge ◽  
Computing Approach

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.

A Novel Edge Detection Algorithm for Medical Image

2012 ◽  
Vol 151 ◽  
pp. 653-656
Author(s):  
Zhan Chun Ma ◽  
Xiao Mei Ning
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Detection Algorithm ◽  
Canny Operator ◽  
Edge Detection Algorithm ◽  
Image Edge Detection ◽  
Traditional Algorithm ◽  
Image Edge ◽  
Improved Algorithm

CANNY operator had widely usage for edge detection; however it also had certain deficiencies. So the traditional CANNY operator about this is improved and puts forward a kind of new algorithm used for image edge detection. Compared improved algorithm with traditional algorithm for edge detection, simulations shows that new algorithm is more effective for image edge detection and the clearer detection result is obtained.

A New Medical Image Edge Detection Algorithm Based on BC-ACO

2017 ◽  
Vol 31 (03) ◽  
pp. 1757002
Author(s):  
Jingyu Zhang ◽  
Jianfu Teng ◽  
Yu Bai
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Ant Colony Algorithm ◽  
Bacterial Chemotaxis ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Ant Colony ◽  
Image Edge Detection ◽  
Image Edge ◽  
Improved Ant Colony Algorithm

Taking the improved ant colony algorithm based on bacterial chemotaxis as a means, this paper proposes one new swarm intelligence optimization algorithm to solve the medical image edge detection problem. The improved ant colony algorithm based on bacterial chemotaxis mainly aims at the shortcoming that the basic ant colony algorithm lacks initial pheromone, and combines bacterial chemotaxis algorithm with basic ant colony algorithm. Firstly, feasible better solution can be found through bacterial chemotaxis algorithm and fed back as initial pheromone. Then ant colony algorithm is implemented to search for the global optimal solution. The algorithm test indicates that the improved ant colony algorithm is more effective in the aspects of searching precision, reliability, optimization speed and stability compared with basic ant colony algorithm. Finally, the improved ant colony algorithm is applied into the edge detection of medical image. It can be seen from the computer simulation that compared with other operators and basic ant colony algorithm on the issue of solving medical image edge detection, the improved ant colony algorithm has superiority and the detected edge is clearer.

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