基于G-SA-SVM的快速血管化鉴别方法
Received:July 23, 2017  Revised:September 20, 2017  点此下载全文
引用本文:罗旭,田望晓,黄怡,吴秀玲,李林辉,陈朋,朱新国,李庆利,褚君浩.基于G-SA-SVM的快速血管化鉴别方法[J].Journal of Infrared and Millimeter Waves,2018,37(1):98~105
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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  
基金项目:国家自然科学基金[微阵列光谱成像的肿瘤多靶点多时相虚拟显微分析研究(61377107);浙江省省自然科学基金[PLADM联合脂肪源间充质干细胞诱导功能性汗腺再生研究](Y16H110002)
中文摘要:生物材料的显微高光谱成像分析技术是生物光谱学研究的前沿.烧伤、深度创伤病人治疗过程中,需要确定移植于患者创面的真皮替代物有没有进入正常的血管化进程,这是评价填充修复材料优劣的关键,也是患者创面恢复的重要指标.提出并实现了一种基于G-SA-SVM的快速血管化鉴别方法.该方法以显微高光谱成像技术为基础,首先对采集的高光谱数据进行光谱维和空间维的空白校正处理,然后对数据进行特征自适应性Gamma校正,最后利用模拟退火优化参数的支持向量机算法(SA-SVM)进行识别处理,有效定位红细胞,进而快速定位血管.实验结果表明,本文提出的G-SA-SVM算法误判率更低,识别精度更高,可以用于微血管新生的评价和鉴定.
中文关键词:关 键 词:血管化  显微高光谱成像  G-SA-SVM
 
Rapid vascularization identification using adaptive Gamma correction and support vector machine based on simulated annealing
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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