摘要：
In image guided surgery, the registration of pre- and intra-operative image data is an important issue. In registrations, we seek an estimate of the transformation that registers the reference image and test image by optimizing their metric function (similarity measure). To date, local optimization techniques, such as the gradient decent method, are frequently used for medical image registrations. But these methods need good initial values for estimation in order to avoid the local minimum. Recently several global optimization methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have been proposed for medical image registration. In this paper, we propose a new approach named hybrid particle swarm optimization (HPSO) for 3-D medical image registration, which incorporates two concepts (subpopulation and crossover) of genetic algorithms into the conventional PSO. Experimental results with both mathematic test functions and medical volume data show that the proposed HPSO performs much better results than conventional gradient decent method, GA and PSO.
摘要：
Historically, height-diameter models have mainly been developed for mature trees; consequently, few height-diameter models have been calibrated for young forest stands. In order to develop equations predicting the height of trees with small diameters, 46 individual height-diameter models were fitted and tested in young black spruce (Picea mariana) and jack pine (Pinus banksiana) plantations between the ages of 4 to 8 years, measured from 182 plots in New Brunswick, Canada. The models were divided into 2 groups: a diameter group and a second group applying both diameter and additional stand- or tree-level variables (composite models). There was little difference in predicting tree height among the former models (Group I) while the latter models (Group II) generally provided better prediction. Based on goodness of fit (R-2 and MSE), prediction ability (the bias and its associated prediction and tolerance intervals in absolute and relative terms), and ease of application, 2 Group II models were recommended for predicting individual tree heights within young black spruce and jack pine forest stands. Mean stand height was required for application of these models. The resultant tolerance intervals indicated that most errors (95%) associated with height predictions would be within the following limits (a 95% confidence level): [-0.54 m, 0.54 m] or [-14.7%, 15.9%] for black spruce and [-0.77 m, 0.77 m] or [-17.1%, 18.6%] for jack pine. The recommended models are statistically reliable for growth and yield applications, regeneration assessment and management planning.
作者机构：
[左海军; 王梓; 刘健] Key Laboratory for Silviculture and Conservation, Beijing Forestry University, Beijing 100083, China;College of Post-Graduate, Central South University of Forestry and Technology, Changsha 410004, China;[王瑞辉] Key Laboratory for Silviculture and Conservation, Beijing Forestry University, Beijing 100083, China, College of Post-Graduate, Central South University of Forestry and Technology, Changsha 410004, China
通讯机构：
[Wang, R.-H.] K;Key Laboratory for Silviculture and Conservation, Beijing Forestry University, China
