基于多图像特征的遥感图像配准新方法

针对不同视角遥感图像配准中的非刚性几何畸变造成的配准误差,文章提出了一种基于尺度不变特征变换(SIFT)特征距离和几何结构描述符的精确方法,用于解决中等地形起伏和成像视点变化条件下遥感图像的配准问题.该方法采用尺度不变特征变换和部分强度不变特征描述符提取可靠的特征点集,利用变换过程中约束几何结构的多图像特征,提出一种新的算法来估计点集之间的精确对应关系.包括特征描述符提取、基于几何结构约束的改进SIFT特征点集配准和非刚性图像变换等步骤.对不同视点的无人机图像和卫星图像的实验结果表明,该方法相对于目前几种先进方法具有更佳的配准性能.

小型内窥镜干涉型光纤陀螺仪装配质量控制系统（英文）

提出一种用于干涉型光纤陀螺仪(IFOG)装配质量控制的内窥镜系统设计及相关图像分析方法。同时采用1.0mm与2.4mm工业内窥镜系统采集IFOG内部图像与视频数据;并利用多自由度运动控制平台调节内窥镜工作姿态以保证图像的顺利采集。在此基础上,设计了基于db4小波基的图像增强方法,基于灰度共生矩阵的图像纹理特征估计方法以及图像几何形状特征的计算方法等,用以进行内窥镜图像的分析。通过上述系统与方法的应用,能够有效检测出IFOG产品在装配过程中出现的多余物颗粒、光纤断纤或异常胶粘等质量问题,有效提高了IFOG产品的装配质量。

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.

Value of Multi-slice spiral CT in diagnosis of malignant gastrointestinal stromal tumors

Objective： The aim of this study was to investigate the value of multi-slice spiral CT （MSCT） in the diagnosis of malignant gastrointestinal stromal tumors （GISTs）. Methods： Twenty-seven cases of MSCT images of malignant GIST proved by surgery and pathology were retrospectively analyzed. Both plain and enhanced CT scan was performed and multiplanar reconstruction was made in all cases. Results： The lesions originated from the stomach （n = 11）, small intestine （n = 9）, colon （n = 4）, rectum （n = 1）, and mesentery （n = 2）. The transverse diameters of mass were 4.2-22 cm, the edges clearly （n = 12）, unclearly （n = 15）. The mass were mainly irregular in shape Iobulated （n = 19）. The lesions were mainly heterogeneity on plain scan, moderate to marked enhancement in arterial phase and durative enhanced in venous phase. Cystic necrosis were observed in all the lesions, 9 cases were cystic and solid mixed mass. Hepatic metastases （n = 4）, pulmonary metastasis （n = 1）, lymphatic metastasis （n = 2） were detected. The accuracy rate of MSCT diagnosis for location and pathologic features of GISTs were 85.2% （23/27） and 77.8% （21/27）. Conclusion： Two-phase MSCT examination and axial images combined with multiplanar reconstruction images have important value for diagnosis of malignant GIST.

Contrast-enhanced multiple-phase imaging features in hepatic epithelioid hemangioendothelioma

AIM： To investigate and review the contrast-enhanced multiple-phase computed tomography （CEMP CT） and magnetic resonance imaging （MRI） findings in patients with pathologically confirmed hepatic epithelioid hemangioendothelioma （HEHE）. METHODS： Findings from imaging examinations in 8 patients （5 women and 3 men） with pathologically confirmed HEHE were retrospectively reviewed （CT images obtained from 7 patients and MR images obtained from 6 patients）. The age of presentation varied from 27 years to 60 years （average age 39.8 years）. RESULTS： There were two types of HEHE： multifocal type （n = 7） and diffuse type （n = 1）. Tn the multifocal-type cases, there were 74 lesions on CT and 28 lesions on MRI with 7 lesions found with diffusion weighted imaging; 18 （24.3%） of 74 lesions on plain CT and 26 （92.9%） of 28 lesions on pre-contrast MRI showed the target sign. On CEMP CT, 28 （37.8%） of 74 lesions appeared with the target sign and a progressive-enhancement rim and 9 （12.2%） of 74 lesions displayed progressive enhancement, maintaining a state of persistent enhancement. On CEMP MRI, 27 （96.4%） of 28 lesions appeared with the target sign with a progressive-enhancement rim and 28 （100%） of 28 lesions displayed progressive-enhancement, maintaining a state of persistent enhancement. In the diffuse-type cases, an enlarged liver was observed with a large nodule appearing with persistent enhancement on CEMP CT and MRI. CONCLUSION： The most important imaging features of HEHE are the target sign and/or progressive en- hancement with persistent enhancement on CEMP CT and MRI. MRI is advantageous over CT in displaying these imaging features.

PROJECTION BASED STATISTICAL FEATURE EXTRACTION WITH MULTISPECTRAL IMAGES AND ITS APPLICATIONS ON THE YELLOW RIVER MAINSTREAM LINE DETECTION

Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.