Rapid vascularization identification using adaptive Gamma correction and support vector machine based on simulated annealing
Received:July 23, 2017  Revised:September 20, 2017  download
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Author NameAffiliationE-mail
LUO Xu Soochow UniversityThe first hospital affiliated to Soochow University 294852961@qq.com 
TIAN Wang-Xiao East China normal university, Shanghai key laboratory of multi-dimensional information processing  
HUANG Yi East China normal university, Shanghai key laboratory of multi-dimensional information processing  
WU Xiu-Lin The first hospital affiliated to wenzhou medical University  
LI Lin-Hui wenzhou medical University  
CHEN Peng wenzhou medical University  
ZHU Xin-Guo Soochow UniversityThe first hospital affiliated to Soochow University zxg45@hotmail.com 
LI Qin-Li East China normal university, Shanghai key laboratory of multi-dimensional information processing  
CHU Jun-Hao East China normal university, Shanghai key laboratory of multi-dimensional information processing  
Abstract:Microscopic hyperspectral imaging technology of biological material is the forefront of biological spectroscopy study. It is important to make sure whether the dermal substitute transplanted in patient’s wounds gets into normal vascularization process when burned or deeply traumatic patients are treated. This is the key to evaluating the quality of repair material and is also an important index of patient’s wounds recovery. This paper proposes and realizes a method of rapid vascularization identification based on G-SA-SVM. This method is based on the microscopic hyperspectral imaging. First, the blank correction is used in hyperspectral data. Second, an adaptive Gamma correction model is employed to take advantage of the spectral and spatial features. Finally, simulated annealing is used to optimize the parameters of support vector machine (SA-SVM). SA-SVM is applied to locating the red blood cells effectively and then locating the blood vessels quickly. The experimental results confirm that the proposed method called G-SA-SVM has higher classification accuracy. Hence, it can be applied to evaluating the vascularization process.
keywords:vascularization  process, microscopic  hyperspectral imaging, G-SA-SVM
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Copyright:《Journal of Infrared And Millimeter Waves》