A Novel Medical Image Edge Detection Method Based on Reinforcement Learning and Ant Colony Optimization

2019 ◽  
Vol 9 (1) ◽  
pp. 175-182
Author(s):  
Xinhua Wang ◽  
Jihong Ouyang ◽  
Yungang Zhu ◽  
Haibo Yu ◽  
Helong Li
Keyword(s):  
Reinforcement Learning ◽  
Edge Detection ◽  
Ant Colony Optimization ◽  
Medical Image ◽  
Detection Method ◽  
Ant Colony ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method

scholarly journals An Image Edge Detection Method Based on Improved Ant Colony Algorithm

2011 ◽  
Vol 1 ◽  
pp. 236-240
Author(s):  
Xi Yun Wang ◽  
Pan Feng Huang ◽  
Ying Pings Fan
Keyword(s):  
Edge Detection ◽  
Ant Colony Algorithm ◽  
Detection Method ◽  
Ant Colony ◽  
Improved Method ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method ◽  
Background Image ◽  
Improved Ant Colony Algorithm

This paper raises an improved ant colony algorithm, for the detection of weak edge of complex background image, considering edge positioning accuracy, edge pixels, edge continuity and interference edges. This algorithm is improved in two aspects: first, we improved the expression of pheromone; second, we improved the calculation of Heuristic information. Compared with traditional Canny detector indicates, the improved method is proved to be accurate in edge detection, good continuity and less interference by experiment.

Research on Improvement Algorithm of Image Edge Detection

2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang
Keyword(s):  
Cognitive Psychology ◽  
Edge Detection ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Information ◽  
Image Edge Detection ◽  
Improvement Algorithm ◽  
Image Edge ◽  
Edge Detection Method

This paper presents new theorems Stevens edge detection method based on cognitive psychology on. Firstly, based on the number of the image is decomposed into high-frequency and low-frequency information, and the high-frequency information extracted by subtracting the maximum number of images to the image after the filter, then the amount of high frequency information into psychological cognitive psychology based on Stevenss theorem. The algorithm suppression refined edge after the non-minimum, applications Pillar K-means algorithm to extract image edge. Experimental results show that: the brightness of the image is converted to the amount of psychological edge can better unify under different brightness values.

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