Classification and recognition of texture collagen obtaining by multiphoton microscope with neural network analysis

5th International Conference on Advances in Optoelectronics and Micro/Nano-optics

Wu, Shulian^1 ; Peng, Yuanyuan^1 ; Hu, Liangjun^1 ; Zhang, Xiaoman^1 ; Li, Hui^1

Key Lab of OptoElectronic Science and Technology for Medicine, Ministry of Education

Fujian Provincial, Key Lab of Photonic Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, FUJIAN

350007, China^1

关键词: Classification accuracy;    Classification and recognition;    Collagen structure;    Collagen textures;    Feature extraction methods;    Grey level co-occurrence matrixes;    Second harmonic generation microscopies (SHG);    Texture features;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/680/1/012014/pdf
DOI  :  10.1088/1742-6596/680/1/012014

来源: IOP

【 摘 要 】

Second harmonic generation microscopy (SHGM) was used to monitor the process of chronological aging skin in vivo. The collagen structures of mice model with different ages were obtained using SHGM. Then, texture feature with contrast, correlation and entropy were extracted and analysed using the grey level co-occurrence matrix. At last, the neural network tool of Matlab was applied to train the texture of collagen in different statues during the aging process. And the simulation of mice collagen texture was carried out. The results indicated that the classification accuracy reach 85%. Results demonstrated that the proposed approach effectively detected the target object in the collagen texture image during the chronological aging process and the analysis tool based on neural network applied the skin of classification and feature extraction method is feasible.