中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共4条，第1-4条

帮助

条数/页：排序方式：
	Evaluation of mesh- and binary-based contour propagation methods in 4D thoracic radiotherapy treatments using patient 4D CT images 期刊论文 iSwitch采集 PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2017, 卷号: 36, 页码: 46-53 作者: Ma, Yuanyuan; Liu, Xinguo; Dai, Zhongying; He, Pengbo; Yan, Yuanlin 收藏 \| 浏览/下载：64/0 \| 提交时间：2019/10/09 Contour propagation algorithm 4D radiotherapy Deformable image registration 4D CT
	Evaluation of mesh- and binary-based contour propagation methods in 4D thoracic radiotherapy treatments using patient 4D CT images 期刊论文 OAI收割 PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2017, 卷号: 36, 页码: 46-53 作者: Ma, Yuanyuan; Liu, Xinguo; Dai, Zhongying; He, Pengbo; Yan, Yuanlin \| 收藏 \| 浏览/下载：32/0 \| 提交时间：2018/05/31 Contour propagation algorithm 4D radiotherapy Deformable image registration 4D CT
	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：33/0 \| 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.
	Intelligent MRTD testing for thermal imaging system using ANN (EI CONFERENCE) 会议论文 OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China Sun J.; Ma D. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly we use frame grabber to capture the 4-bar target image data. Then according to image gray scale we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets along with known target visibility are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.