Robust image modeling technique with a bioluminesence image segmentation application

	Robust image modeling technique with a bioluminesence image segmentation application
	Zhong, Jianghong; Wang, Ruiping; Tian, Jie
	2009
会议名称	Progress in Biomedical Optics and Imaging - SPIE
会议录名称	Progress in Biomedical Optics and Imaging - SPIE
会议日期	2009
会议地点	Lake Buena Vista, FL, United states
摘要	A robust pattern classifier algorithm for the variable symmetric plane model, where the driving noise is a mixtureof a Gaussian and an outlier process, is developed. The veracity and high-speed performance of the pattern recognition algorithm is proved.Bioluminescence tomography (BLT) has recently gained wide acceptance in the field of in vivo small animal molecular imaging. So that it is very important for BLT to how to acquire the highprecision region of interest in a bioluminescence image (BLI) in order to decrease loss of the customers because of inaccuracy in quantitative analysis. An algorithm in the mode is developed to improve operation speed, which estimates parameters and original image intensity simultaneously from the noise corrupted image derived from the BLT optical hardware system. The focus pixel value is obtained from the symmetric plane according to a more realistic assumption for the noise sequence in the restored image. The size of neighborhood is adaptive and small. What’s more, the classifier function is base on the statistic features. If the qualifications for the classifier are satisfied, the focus pixel intensity is setup as the largest value in the neighborhood.Otherwise, it will be zeros.Finally,pseudo-color is added up to the result of the bioluminescence segmented image. The whole process has been implemented in our 2D BLT optical system platform and the model is proved.
关键词	Robust Image Classifier Segmentation Blt Region Of Interest
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/5441
专题	中国科学院分子影像重点实验室
通讯作者	Tian, Jie
推荐引用方式 GB/T 7714	Zhong, Jianghong,Wang, Ruiping,Tian, Jie. Robust image modeling technique with a bioluminesence image segmentation application[C],2009.

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