基于配准的插值

断层间图像插值是三维重建的一个关键步骤,因为图像上像素之间的间隔常常小于断层图像之间的距离,而在三维重建需要它们有一致的分辨率.由于是同模态断层图像层间插值,对于解决同模态弹性配准问题,Thirion的demons算法比较适合.所以配准采用Demons方法.Demons算法先判断出待配准图像上各个象素点的运动方法,通过对各个象素点的移动来实现非刚性配准.在这个算法中,每张图像都被视为同灰度值轮廓的集合.该算法可以应用于精度要求比较高的体数据插值重建过程.     

使用Radon变换进行二维MRI图像配准

使用了一种基于Radon变换的技术来进行二维的MRI图像配准。MRI的图像配准一般使用灰度配准,而Radon变换一般用于CT图像的重建,虽然现已经存在使用Radon变换进行图像配准,但是比较繁琐,我们对这一配准算法进行了简化。     

医学图像融合配准技术

图像融合技术在现代医学中扮演着极其重要的角色,是现代医学图像技术研究的重点。图像融合技术中,图像的配准又是其中的重点、难点和热点。本文按照图像变换特性对图像配准进行了分类,对每个类别的不同配准方法(特征点的获取、图像配准的变换等)进行介绍。但是,图像配准是一个尚处在发展阶段的学科,实现配准的精确化、快速化、自动化仍需要进一步的努力。     

肺癌影像引导放疗人工图像配准方法分析

目的分析千伏锥形束CT（KVCBCT）引导肺癌放疗人工图像配准法的重复性。方法选择16例在我院行根治性放疗的非小细胞肺癌患者,每周行KVCBCT在线引导体位校正一次,获取患者KVCBCT影像。图像配准选择肺尖和椎体作为参考标记,在矢状位、冠状位和横断位等中心层面上配准患者KVCBCT影像和计划设计cT影像。比较同一名医生相隔一周两次配准,不同医生之间配准和医生与技术员之间配准结果的差异,用于评价KVCBCT引导肺癌放疗人工图像配准法的重复性。结果同一位医生相隔一周两次配准同一幅KVCBCT影像与计划设计cT影像,配准结果在患者左右（LR）、头脚（sI）和前后（AP）三个方向上,差值大于3mm所占的比例分别为：0,13％和6％。不同医生之间的配准结果在LR、SI和AP三个方向上,差值大于3mm所占的比例分别为：11％,19％和14％。医生与技术员的配准结果在LR、SI和AP三个方向上差值大于3mm所占的比例,分别为：16％,27％和27％。结论KVCBCT引导肺癌放疗人工图像配准法的重复性有待进一步提高。尤其表现为不同医生,医生与技术员之间应用该方法的重复性较差。KVCBCT引导肺癌放疗的图像配准方法需要进一步研究。     

共聚焦激光检眼镜下眼底图像的非特征配准约束模型研究

研究共聚焦激光检眼镜下不基于特征提取的眼底图像自动配准方法中的运动约束模型,从成像机理上分析共聚焦激光检眼镜下图像对间的运动模式,并分析比较多种实际全局运动模型约束下的配准精度和效率,进而给出一种由粗到细的复合约束模型对眼底图像进行配准。实验结果证实了该模型效果良好。     

基于非刚体配准的体数据插值重建方法

三维图像的处理和操作需要将一般的断层序列插值成为具有各坐标轴一致的分辨率的体数据,而目前最常用的线性插值方法在层间距较大时会导致图像边缘模糊和出现伪影。Penney根据现有的非刚体匹配方法,提出了利用图像形变场数据的插值算法,大大提高了层间插值的质量。本文对Penney提出的算法进行了两方面的改进,在配准过程中用简单的单射性约束取代了复杂的平滑性约束,用邻域平均算法替代Penney使用的最邻近直线插值方法,并将新算法的实验结果与原算法、线性插值进行了对比,新算法在保持高质量插值的前提下提高了计算速度。该算法可以应用于精度要求比较高的体数据插值重建过程。     

自适应指数加权互信息配准医学图像的测度及相应算法

为配准医学图像,本文提出了一种新的自适应指数加权的互信息(Adaptive Exponential Weighted Mutual Informa- tion,AEWMI)测度,分析表明:通过对互信息(Mutual Information,MI)测度进行指数加权可以提高测度曲线的峰值尖锐性和平滑性;而指数的权值则可以通过评估待配准图像的质量和分辨率大小来自适应确定。仿真实验结果在验证分析结果的同时也表明,基于本文AEWMI测度的配准方案,对图像噪声、分辨率差异等有较高的鲁棒性,且可有效地提高配准的成功率。     

自适应放射治疗中的图像变形配准关键技术研究

放射治疗是恶性肿瘤的主要治疗手段之一,随着肿瘤放疗技术的不断发展,因器官运动、变形等引发的问题越来越突出,在此背景下,自适应放射治疗得以广泛推广和应用。本文主要对自适应放射治疗中的图像变形配准关键技术进行分析,重点对CT和信息缺失CBCT图像的变形配准问题进行探讨,以期能够对图像变形配准问题的解决提供一定的指导。     

DSA图像中运动伪影的消除方法

由于病人存在着各种运动(如呼吸、肌肉运动、心脏运动、设备噪声),在成像过程中常会造成图像上出现伪影,干扰医生的正常诊断,为消除这种伪影,本文提出一种基于图像配准思想的全自动消除伪影的方法,该方法能够自动消除DSA图像中的大部分运动伪影,使DSA图像得到较好的增强,并为后面的血管分割和三维重建提供便利,是一种快速有效的方法。     

基于图像配准分析物种进化关系的新方法

图像配准是图像处理的一个重要技术,可用于分析两幅图像之间的相似度。本文提出了一种基于图像配准分析物种进化关系的新方法:首先利用一阶马尔可夫链方法计算不同基因组序列的寡聚核苷酸转移概率矩阵;然后将转移概率矩阵转换为彩色图像矩阵,并绘制物种两两之间彩色图像矩阵的联合直方图;最后分析联合直方图点集的分布情况,引入直方图点集的散度公式,将其作为相似性测度的标准,从而鉴定物种亲缘关系的远近。100种细菌全基因组的计算结果表明,相较于单基因法或基于基因组寡聚核苷酸频率组分差异信息的方法,本文提出的新方法具有更高的准确度和分辨力,它不仅能够很好地分辨科以下的分类单元,而且对科以上的分类单元同样具有较好的区分效果。该方法有望发展成为物种鉴定及系统发育推断的有效手段。     

Culture conditions optimization of hyaluronic acid production by Streptococcus zooepidemicus based on radial basis function neural network and quantum-behaved particle swarm optimization algorithm

This study aimed to optimize the culture conditions (agitation speed, aeration rate and stirrer number) of hyaluronic acid production by Streptococcus zooepidemicus. Two optimization algorithms were used for comparison: response surface methodology (RSM) and radial basis function neural network coupling quantum-behaved particle swarm optimization algorithm (RBF-QPSO). In RBF-QPSO approach, RBF is employed to model the microbial HA production and QPSO algorithm is used to find the optimal culture conditions with the established RBF estimator as the objective function. The predicted maximum HA yield by RSM and RBF-QPSO was 5.27 and 5.62 g/l, respectively, while a maximum HA yield of 5.21 and 5.58 g/l was achieved in the validation experiments under the optimal culture conditions obtained by RSM and RBF-QPSO, respectively. It was indicated that both models provided similar quality predictions for the above three independent variables in terms of HA yield, but RBF model gives a slightly better fit to the measured data compared to RSM model. This work shows that the combination of RBF neural network with QPSO algorithm has good predictability and accuracy for bioprocess optimization and may be helpful to the other industrial bioprocesses optimization to improve productivity.     

Automatic registration and error detection of multiple slices using landmarks.

OBJECTIVES: When analysing the 3D structure of tissue, serial sectioning and staining of the resulting slices is sometimes the preferred option. This leads to severe registration problems. In this paper, a method for automatic registration and error detection of slices using landmark needles has been developed. A cost function takes some parameters from the current state of the problem to be solved as input and gives a quality of the current solution as output. The cost function used in this paper, is based on a model of the slices and the landmark needles. The method has been used to register slices of prostates in order to create 3D computer models. Manual registration of the same prostates has been undertaken and compared with the results from the algorithm. METHODS: Prostates from sixteen men who underwent radical prostatectomy were formalin fixed with landmark needles, sliced and the slices were computer reconstructed. The cost function takes rotation and translation for each prostate slice, as well as slope and offset for each landmark needle as input. The current quality of fit of the model, using the input parameters given, is returned. The function takes the built-in instability of the model into account. The method uses a standard algorithm to optimize the prostate slice positions. To verify the result, s standard method in statistics was used. RESULTS: The methods were evaluated for 16 prostates. When testing blindly, a physician could not determine whether the registration shown to him were created by the automated method described in this paper, or manually by an expert, except in one out of 16 cases. Visual inspection and analysis of the outlier confirmed that the input data had been deformed. The automatic detection of erroneous slices marked a few slices, including the outlier, as suspicious. CONCLUSIONS: The model based registration performs better than traditional simple slice-wise registration. In the case of prostate slice registration, other aspects, such as the physical slicing method used, may be more important to the final result than the selection of registration method to use.     

Accelerating Neuroimage Registration through Parallel Computation of Similarity Metric

Neuroimage registration is crucial for brain morphometric analysis and treatment efficacy evaluation. However, existing advanced registration algorithms such as FLIRT and ANTs are not efficient enough for clinical use. In this paper, a GPU implementation of FLIRT with the correlation ratio (CR) as the similarity metric and a GPU accelerated correlation coefficient (CC) calculation for the symmetric diffeomorphic registration of ANTs have been developed. The comparison with their corresponding original tools shows that our accelerated algorithms can greatly outperform the original algorithm in terms of computational efficiency. This paper demonstrates the great potential of applying these registration tools in clinical applications.     

Nonrigid Registration Regularized by Shape Information: Application to Atlas Construction of Cardiac CT Images

Cardiac atlases play an important role in the computer-aided diagnosis of cardiovascular diseases, in particular they need to deal with large and highly variable image datasets. In this paper, we propose a new nonrigid registration algorithm incorporating shape information, to produce comprehensive atlases. For one thing, the multiscale gradient orientation features of images are combined to form the construction of multifeature mutual information. Additionally, the shape information of multiple-objects in images is incorporated into the cost function for registration. We demonstrate the merits of the new registration algorithm on the 3D data sets of 15 patients. The experimental results show that the new registration algorithm can outperform the conventional intensity-based registration method. The obtained atlas can represent the cardiac structures more accurately.     

Phase correlation applied to the 3D registration of CT and CBCT image volumes

PurposeIn this study, a 3D phase correlation algorithm was investigated to test feasibility for use in determining the anatomical changes that occur throughout a patient's radiotherapy treatment. The algorithm determines the transformations between two image volumes through analysis in the Fourier domain and has not previously been used in radiotherapy for 3D registration of CT and CBCT volumes.MethodsVarious known transformations were applied to a patient's prostate CT image volume to create 12 different test cases. The mean absolute error and standard deviation were determined by evaluating the difference between the known contours and those calculated from the registration process on a point-by-point basis. Similar evaluations were performed on images with increasing levels of noise added. The improvement in structure overlap offered by the algorithm in registering clinical CBCT to CT images was evaluated using the Dice Similarity Coefficient (DSC).ResultsA mean error of 2.35 (σ = 1.54) mm was calculated for the 12 deformations applied. When increasing levels of noise were introduced to the images, the mean errors were observed to rise up to a maximum increase of 1.77 mm. For CBCT to CT registration, maximum improvements in the DSC of 0.09 and 0.46 were observed for the bladder and rectum, respectively.ConclusionsThe Fourier-based 3D phase correlation registration algorithm investigated displayed promising results in CT to CT and CT to CBCT registration, offers potential in terms of efficiency and robustness to noise, and is suitable for use in radiotherapy for monitoring patient anatomy throughout treatment.     

Rapid pedobarographic image registration based on contour curvature and optimization

Image registration, the process of optimally aligning homologous structures in multiple images, has recently been demonstrated to support automated pixel-level analysis of pedobarographic images and, subsequently, to extract unique and biomechanically relevant information from plantar pressure data. Recent registration methods have focused on robustness, with slow but globally powerful algorithms. In this paper, we present an alternative registration approach that affords both speed and accuracy, with the goal of making pedobarographic image registration more practical for near-real-time laboratory and clinical applications. The current algorithm first extracts centroid-based curvature trajectories from pressure image contours, and then optimally matches these curvature profiles using optimization based on dynamic programming. Special cases of disconnected images (that occur in high-arched subjects, for example) are dealt with by introducing an artificial spatially linear bridge between adjacent image clusters. Two registration algorithms were developed: a ‘geometric’ algorithm, which exclusively matched geometry, and a ‘hybrid’ algorithm, which performed subsequent pseudo-optimization. After testing the two algorithms on 30 control image pairs considered in a previous study, we found that, when compared with previously published results, the hybrid algorithm improved overlap ratio (p=0.010), but both current algorithms had slightly higher mean-squared error, assumedly because they did not consider pixel intensity. Nonetheless, both algorithms greatly improved the computational efficiency (25±8 and 53±9 ms per image pair for geometric and hybrid registrations, respectively). These results imply that registration-based pixel-level pressure image analyses can, eventually, be implemented for practical clinical purposes.     

Global Registration of Subway Tunnel Point Clouds Using an Augmented Extended Kalman Filter and Central-Axis Constraint

Because tunnels generally have tubular shapes, the distribution of tie points between adjacent scans is usually limited to a narrow region, which makes the problem of registration error accumulation inevitable. In this paper, a global registration method is proposed based on an augmented extended Kalman filter and a central-axis constraint. The point cloud registration is regarded as a stochastic system, and the global registration is considered to be a process that recursively estimates the rigid transformation parameters between each pair of adjacent scans. Therefore, the augmented extended Kalman filter (AEKF) is used to accurately estimate the rigid transformation parameters by eliminating the error accumulation caused by the pair-wise registration. Moreover, because the scanning range of a terrestrial laser scanner can reach hundreds of meters, a single scan can cover a tunnel segment with a length of more than one hundred meters, which means that the central axis extracted from the scan can be employed to control the registration of multiple scans. Therefore, the central axis of the subway tunnel is first determined through the 2D projection of the tunnel point cloud and curve fitting using the RANSAC (RANdom SAmple Consensus) algorithm. Because the extraction of the central axis by quadratic curve fitting may suffer from noise in the tunnel points and from variations in the tunnel, we present a global extraction algorithm that is based on segment-wise quadratic curve fitting. We then derive the central-axis constraint as an additional observation model of AEKF to optimize the registration parameters between each pair of adjacent scans. The proposed approach is tested on terrestrial point clouds that were acquired in a subway tunnel. The results show that the proposed algorithm is capable of improving the accuracy of aligning multiple scans by 48%.     

Optical-CT Dual-Modality Mapping Base on DRR Registration

Optical-CT dual-modality imaging requires the mapping between 2D fluorescence images and 3D body surface light flux. In this paper, we proposed an optical-CT dual-modality image mapping algorithm based on the Digitally Reconstructed Radiography (DRR) registration. In the process of registration, a series of DRR images were computed from CT data using the ray casting algorithm. Then, the improved HMNI similarity strategy based on Hausdorff distance was used to complete the registration of the white-light optical images and DRR virtual images. According to the corresponding relationship obtained by the image registration and the Lambert’s cosine law based on the pin-hole imaging model, the 3D light intensity distribution on the surface of the object could be solved. The feasibility and effectiveness of the mapping algorithm are verified by the irregular phantom and mouse experiments.     

Enhanced α-amylase production by a marine protist,Ulkenia sp. using response surface methodology and genetic algorithm

Amylases are a group of enzymes with a wide variety of industrial applications. Enhancement of α-amylase production from the marine protists, thraustochytrids has been attempted for the first time by applying statistical-based experimental designs using response surface methodology (RSM) and genetic algorithm (GA) for optimization of the most influencing process variables. A full factorial central composite experimental design was used to study the cumulative interactive effect of nutritional components viz., glucose, corn starch, and yeast extract. RSM was performed on two objectives, that is, growth of Ulkenia sp. AH-2 (ATCC® PRA­296) and α-amylase activity. When GA was conducted for maximization of the enzyme activity, the optimal α-amylase activity was found to be 71.20?U/mL which was close to that obtained by RSM (71.93?U/mL), both of which were in agreement with the predicted value of 72.37 U/mL. Optimal growth at the optimized process variables was found to be 1.89A_660nm. The optimized medium increased α-amylase production by 1.2-fold.     

Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO₄, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.