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	Sparse self-attention aggregation networks for neural sequence slice interpolation 期刊论文 OAI收割 BioData Mining, 2021, 卷号: 14, 期号: 1, 页码: 19 作者: Wang,Zejin; Liu,Jing; Chen,Xi; Li,Guoqing; Han,Hua \| 收藏 \| 浏览/下载：36/0 \| 提交时间：2021/02/18 Slice interpolation Biological tissue recovery EM images Sparse self-attention network Adaptive style-balance loss
	A fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images 期刊论文 OAI收割 BIODATA MINING, 2018, 卷号: 11, 页码: 15 作者: Li, Weifu; Liu, Jing; Xiao, Chi; Deng, Hao; Xie, Qiwei \| 收藏 \| 浏览/下载：46/0 \| 提交时间：2019/01/08 3D connection EM images Bwconncomp Mitochondria Synapse
	Contour extracting with combination particle filtering and em algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging, ISPDI 2007: Related Technologies and Applications, September 9, 2007 - September 12, 2007, Beijing, China Meng B.; Zhu M. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 The problem of extracting continuous structures from images is a difficult issue in early pattern recognition and image processings[1]. Tracking with contours in a filtering framework requires a dynamical model for prediction. Recently Particle filter is widely used because its multiple hypotheses and versatility within framework. However the good choice of the propagation function is still its main problem. In this paper an improved particle filter EM-PF algorithm is proposed which using the EM (Expectation-Maximization) algorithm to learn the dynamical models. The EM algorithm can explicitly learn the parameters of the dynamical models from training sequences. The advantage of using the EM algorithm in particle filter is that it is capable of improve tracking contour by having accurate model parameters. Though the experiment results we show how our EM-PF can be applied to produces more robust and accurate extracting.
	CR image filter methods research based on wavelet-domain hidden markov models (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang J.-L.; Wang J.-L.; Li D.-Y.; Wang Y.-P. 收藏 \| 浏览/下载：18/0 \| 提交时间：2013/03/25 In the procedure of computed radiography imaging we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail the author think that the major two noises are Gaussian white noise and Poisson noise. Then the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images a multiscale image filtering algorithm which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM according to scale size and achieve the experiments as well as the comparison with other denoising methods were presented at last.