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| Contour Extraction Method of Colony Images Based on Dynamic Synaptic Neural Network |
| Cai Zhefei, Fan Yingle*, Wu Wei |
| (Laboratory of Pattern Recognition and Image Processing, Hangzhou Dianzi University, Hangzhou 310018, China) |
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Abstract The accuracy of colony image contour extraction is of great significance for microbial colony morphology and feature analysis. Based on the dynamic synaptic neural network, we constructed an image contour extraction method for colonies. Firstly, simulating the photoelectric conversion process of retinal, proposed an adaptive adjustment model of receptive field scale based on light intensity. Secondly, a LIF model combining the lateral regulation of electrical synapse and chemical synapse was constructed. Using neuron membrane potential, spatial distribution relationship and response time difference to adjust the strength of electrical synapse and chemical synapse, the colony edge sensitive image with or without lateral regulation was obtained. Finally, compared the difference of neurons response time with or without lateral regulation, and using STDP rules to dynamically update the weight of neuronal synaptic, so as to adjust the contour details of colony images. The adjusted contour response with the primary contour response were combined to obtain the final contour information of colony images. Taking 40 colony images collected in the laboratory as research objects, and selecting edge confidence BIdx, average reconstructed similarity MSSIM and comprehensive index EIdx as the evaluation indexes. The results showed that the contours obtained by this study was more accurate, continuous and has less noise. BIdx and MSSIM were 0.651 4±0.056 5 and 0.831 8±0.026 1, respectively. Meanwhile, EIdx was 0.765 7±0.027 4, which was significantly higher (P<0.01) than that of biological vision based methods of OS, BAR and LS. The dynamic synaptic neural network constructed in this paper is suitable for image contour extraction with rich detailed features such as colonies and may provide a new way for the research and application of neural computing models that integrate the biological vision mechanism.
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Received: 24 November 2021
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Corresponding Authors:
*E-mail: fan@hdu.edu.cn
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