Applications of improved artificial fish swarm algorithm in microscopy autofocus

Jiang Minshan; Yan Jin; Xu Xiaoli; Zhang Xuedian

doi:10.12086/oee.2018.180236

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Jiang Minshan, Yan Jin, Xu Xiaoli, et al. Applications of improved artificial fish swarm algorithm in microscopy autofocus[J]. Opto-Electronic Engineering, 2018, 45(12): 180236. doi: 10.12086/oee.2018.180236

Citation:

Jiang Minshan, Yan Jin, Xu Xiaoli, et al. Applications of improved artificial fish swarm algorithm in microscopy autofocus[J]. Opto-Electronic Engineering, 2018, 45(12): 180236. doi: 10.12086/oee.2018.180236

Applications of improved artificial fish swarm algorithm in microscopy autofocus

Institute of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Fund Project: Supported by Grants from the National Key Foundation for Exploring Scientific Instrument of China (2013YQ03065104)

More Information

Corresponding author: Zhang Xuedian, E-mail:xdzhang@usst.edu.cn

Received Date 03 May 2018

Revised Date 08 June 2018

Published Date 01 December 2018

Abstract

Abstract

The selection of the focusing window is the key procedure in achieving precise automatic focus of the microscope. For the traditional focus window selection method, the limitation is that the target object cannot be accurately positioned. This paper proposes an improved artificial fish focusing window method. The method takes the area-of-interest of the whole image as the basis of the selection window. Through utilizing the global optimization ability of the artificial fish swarm algorithm, the best focusing window can be obtained. Adding the global optimal value to the behavior update of each artificial fish makes the artificial fish quickly move to the optimal position. Under the premise of ensuring accuracy, the elimination behavior is introduced to improve the convergence speed of the algorithm in the later period. Furthermore, according to the characteristics of the bulletin board in the algorithm, the interference area is identified with the trend comparison method, and the influence of the non-target area is effectively excluded. Experiment results show that the focusing window obtained by this algorithm can be well-suited for the target object, greatly improve the accuracy of autofocus, and make the efficiency improvement 1.65 times than the traditional method.
- autofocus /
- focus window /
- region selection algorithm /
- artificial fish swarm algorithm /
- image processing

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References

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Overview

Overview

Overview: As the computer is developed in acquiring and analyzing images, microscope systems become available to focus automatically. The selection of the focusing window is the key procedure achieving precise autofocus. A suitable focusing window can save time and focus more consistently than the entire image in the focusing process. Traditional focus window selection methods were designed to solve the problem of large amounts data for microscopes. However, the conventional methods cannot position the target object accurately to some extent. This paper proposes an improved artificial fish swarm focusing window method. Firstly, according to the characteristics of the target object often containing more details than the non-interested area, this method takes the area-of-interest of the whole image as the basis of the selection window. Through utilizing the global optimization ability of the artificial fish swarm algorithm, the best focusing window can be obtained. Secondly, adding the global optimal value to the behavior update of each artificial fish makes the artificial fish quickly move to the optimal position while reducing the time for each artificial fish to select and adjust behavior. Thirdly, under the premise of ensuring accuracy, the elimination mechanism is introduced to the algorithm, and the individual fish with poor performance was deleted to decrease unnecessary calculation. Furthermore, the bulletin board in the algorithm always records the state of the optimal artificial fish. By making use of the feature, the influence of the non-target area is effectively excluded. It then compares this method with traditional focus window methods. It is shown that the proposed method gives the best results when tested on several different sets of specimen images. The focusing window obtained by this algorithm can be well-suited for the target object regardless of whether the target is in the center of the image. Experiment results between the defocus images and the focus images demonstrate that proposed method have good performance about the stability of algorithm. The windows captured under different levels of focus images are basically the same. The superiority of this algorithm is greatly improving the accuracy of autofocus, compared with other recently reported artificial fish swarm focusing window algorithms. Moreover, the method improves the convergence speed of the algorithm in the later period and avoids a tedious parameter-tuning procedure. Simulation results verify that the proposed algorithm can make the efficiency improve 1.65 times than the traditional artificial fish swarm algorithm.