Objective To explore the topological properties of the brain structural network in patients with neuromyelitis optica spectrum disorder (NMOSD).Methods Diffusion tensor imaging was performed in 41 NMOSD patients (patient group) and 40 age-and sex-matched healthy volunteers (control group) who were admitted to the Department of Neurology,The Third Affiliated Hospital to Sun Yat-sen University from September 2014 to October 2017.The deterministic fiber tracking techniques were used to construct the white matter structural weighted network.Topological properties of the brain structural network were then calculated based on complex graph theory analysis.The 2 groups were compared in terms of global and local parameters of the brain structural network using statistical methods.Results The brain structural networks in both groups exhibited small world properties.Compared with the control group,the global efficiency of the brain structural network in the patient group was significantly decreased and the shortest path length significantly increased (P=0.002,P=0.002,FDR correction).There were no statistically significant differences between the brain structural networks of the 2 groups in terms of clustering coefficient,the shortest path length on average,value of small world property,average clustering coefficient or local efficiency (P=0.780,P=0.496,P=0.279,P=0.269,P=0.050,FDR correction).Compared with the control group,the nodal efficiency of the brain structural network of the patient group was significantly decreased in the frontal lobe (bilateral precentral gyrus,middle frontal gyrus of the right orbital part,inferior frontal gyrus of the right opercular part,right rolandic operculum,bilateral median cingulate and paracingulate gyri),parietal lobe (right posterior cingulate gyrus,right superior parietal gyrus,left inferior parietal of angular gyri,right angle gyrus,and right precuneus),temporal lobe (bilateral hippocampus and right parahippocampal gyrus),occipital lobe (left cuneus,left superior occipital gyms,bilateral middle occipital gyrus,and left inferior occipital gyrus) and subcortical region (right caudate nucleus and right thalamus) (P＜0.05,FDR correction).Conclusion There is abnormal connection in brain structural network in NMOSD patients.

An evaluative pipeline was developed to quantitatively assess TBSS,which is widely used in voxelbased diffusion tensor imaging analysis.First,datasets of Alzheimer's disease patient group with white matter injury of different degrees were simulated from the DTI-FA images of 31 normal controls.Then the simulated datasets were analyzed with TBSS.The numbers of the detected voxels changed significantly and the ground-truth changed voxels were obtained.The sensitivity and specificity of the method were thus calculated.Quantitative results showed that the sensitivity of TBSS analysis was increased at the cost of the decrease of specificity.When the decreased FA values in patient group was relatively large,the sensitivity could be up to 90％,while the specificity would decrease to 50％,or even lower.Therefore,when interpreting the TBSS analysis results,other complementary medical studies should be considered to account for the relatively higher false positive rate.

This study aimed to identify a PD-specific MRI pattern using combined diffusion tensor imaging (DTI) and arterial spin labeling (ASL) to discriminate patients with early PD from healthy subjects and evaluate disease status. Twenty-one early and 22 mid-late PD patients, and 22 healthy, age/gender-matched controls underwent 3-T MRI with apparent diffusion coefficient (ADC), fractional anisotropy (FA), fiber number (FN) and cerebral blood flow (CBF) measurements. We found that compared with healthy subjects, there was a profound reduction in FN passing through the SN in PD. FA in the SN and CBF in the caudate nucleus were inversely correlated with motor dysfunction. A negative correlation was observed between FA in the hippocampus (Hip) and the NMSS-Mood score, whereas CBF in the Hip and the prefrontal cortex(PFC) correlated with declined cognition. Stratified five-fold cross-validation identified FA in the SN(FA-SNAv), CBF in the PFC(CBF-PFCAv) and FA in the parietal white matter(FA-PWMAv), and the combination of these measurements offered relatively high accuracy (AUC 0.975, 90% sensitivity and 100% specificity) in distinguishing those with early PD from healthy subjects. We demonstrate that the decreased FNs through SN in combination with changes in FA-SNAv, CBF-PFCAv and FA-PWMAv values might serve as potential markers of early-stage PD.

Aiming at some bottlenecks in the diagnosis of current capsule endoscopy, the screening of redundant image data is conducted for the massive data produced by capsule endoscopy, which increases the doctors' speed of image reading. The bluetooth-based capsule model is realized, and the"self-diagnosis mode"based on capsule endoscopy is put forward on this ground.

We proposed a new stitching method based on sift features to obtain an enlarged view of transmission electron microscopic (TEM) images with a high resolution. The sift features were extracted from the images, which were then combined with fitted polynomial correction field to correct the images, followed by image alignment based on the sift features. The image seams at the junction were finally removed by Poisson image editing to achieve seamless stitching, which was validated on 60 local glomerular TEM images with an image alignment error of 62.5 to 187.5 nm. Compared with 3 other stitching methods, the proposed method could effectively reduce image deformation and avoid artifacts to facilitate renal biopsy pathological diagnosis.

扩散张量成像(DTI)是唯一可在体显示脑白质纤维束的无创成像方法,其利用组织内水分子扩散的各向异性特征进行成像.本文以DTI基本原理为出发点,对DTI的主要研究方向和数据分析方法进行分析.

With the application in many neuroimaging studies, diffusion tensor image (DTI) registration has generated considerable interest and been studied widely. Although a number of DTI registration methods have been developed, their performances have not yet been compared systematically. This work addresses this gap by comparing a large number of existing DTI registration methods and gives the comprehensive evaluation results. In this paper, the open-access IXI DTI dataset were used. In order to compare the accuracy of tensor matching, 11 open-source registration methods were evaluated with 7 quantitative and open-access evaluation criteria that measure the similarity among tensors (namely tensor-based techniques) or scalar images derived from diffusion tensors (namely scalar-based techniques). The evaluation results indicate that the diffeomorphic deformable tensor registration method (referred to as DTI-TK) is the best method, followed by the symmetric image normalization method (referred to as SyN).

To solve the problem of losing detailed edge information inherent in traditional DTI image segmentationmethods, some new morphological gradient tensor parameters are proposed. Based on the tensor similaritymorphological gradients and tensor morphological anisotropic gradients, the tag based on watershed algorithm isapplied in DTI image segmentation. The human brain corpus callosum image segmentation experiments show that thisalgorithm with the TMG-l2 and TMG-RA parameters can quickly and accurately locate and segment the outline ofthe image, and the edge information of the important region is preserved.

为准确检测并量化评估毛刺征,提出一种CT图像肺结节的毛刺检测与量化评估方法。首先利用区域生长算法与水平集方法结合进行结节主体的准确分割;而后利用线性滤波模板提取结节主体周边区域的毛刺;最后引入毛刺水平指数作为毛刺特征的量化指标。在此基础上对结节有无毛刺进行分类,并与肺部图像数据库联盟(LIDC)的量化评级进行一致性和相关性分析。实验结果表明,该方法可以有效地检测并定量描述CT图像肺结节的毛刺征。

Radiographic detection of pulmonary nodules based on three-dimensional Hessian matrix is highly sensitive but frequently produces false positive results in areas where blood vessels intersect. We propose a novel approach to pulmonary nodule detection using Hessian matrix-based adaptive window structure analysis, in which the structure coefficients is used to differentiate a voxel that belongs to a nodule or vascular structures, followed by construction of the 3D adaptive window to analyze the local structure characteristics; the nodules were then detected using the discrimination function. The experimental results on pulmonary CT images from 17 patients showed a 100% detection sensitivity for nodules of varying sizes and types, with also significantly reduced false positive results generated by the vessel junctions. This approach provides valuable assistance to follow-up positioning and segmentation of the pulmonary nodules.

In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis.

Calculating morphological gradient is the key step of watershed algorithm. In this article, new tensorial similarity morphological gradient is defined based on the eight neighborhoods, and new tensor anisotropy morphological gradients are put forward, which are then used in the watershed segmentation framework to segment DTI image. The results of the segmentation experiments on the corpus callosum show that: Compared to other tensor anisotropy morphological gradients based watershed segmentation methods, the one based on newly proposed tensor anisotropy morphological gradients can more quickly and accurately position and depict the segmentation outline of the image, which can also better protect the edge information of the important region.

A comparative study is conducted on the I/O performance between the popular web service applications of Apache and Nginx in high concurrence environment,based on the analysis of linux I/O model and the related I/O event-driven mechanism of Apache and Nginx.The functionality and usage are also compared with an application instance.Both the advantages and disadvantages of Apache and Nginx are discussed.Some suggestions for properly choosing of the 2 applications are given in the end.

In light of heavy workload of going over all the wireless capsule endoscopy(WCE) images,we present a QT-based implementation scheme for WCE image analysis system.Based on classifying the mass WCE images,the system screens in turn the over-dark images and the duplicate images,and recognises the tumour lesions amongst the above filtered results.According to the analysis on the experimental result,the screening manners of either screening by grade or manually inputting the screening proportion have filtered out most of invalid and redundant image data,it greatly improves the film-reading speed of doctors while ensures their diagnosis quality.

OBJECTIVE:To simulate the multi-leaf collimator of Varian linear accelerator using Monte Carlo method.METHODS:The multi-leaf collimator model was established using the DYNVMLC module of BEAMnrc and validated by comparison of Monte Carlo simulation and actual measurement results.RESULTS:The simulation results were well consistent with the actual measurement results with a bias of less than 3%.CONCLUSION:The multi-leaf collimator of Varian linear accelerator can be successfully modeled using Monte Carlo method for analysis of the impact of the geometric properties of the multi-leaf collimator on the dose distribution.

With the broad usage of diffusion tensor imaging (DTI) modality in clinical medical treatment, the DT image segmentation has become a research focus on medical image processing and analysis at home and abroad. In this paper, we reviewed various segmentation methods of DT images in recent years, and mainly investigated the state of the art and recent advances of those based on clustering, graph cuts and level set. Moreover, we discussed the computing procedure of each typical algorithm respectively, analyzed and compared segmentation objects, advantages, disadvantages and similarity metrics of these methods qualitatively. At the end, after summarizing main characteristics of existing methods, we prospected future development trend on DT image segmentation.

This paper proposed an unsupervised algorithm to delete the redundant WCE images,which was based on the analysis of the normalized mutual information and normalized cross-correlation coefficient between the successive frames.The algorithm firstly conducted quantification and clustering in HSV color space.Then,it calculated the similarity metrics between the successive frames.Finally,it iteratively applied deletion procedure according to the prescribed deletion rate.The pathology retaining rate,which was defined as the percentage of the remaining images bearing pathological changes from the total ones was almost 100% with very low mis-deletion rate for 70% prescribed deletion rate of 49 patients.Experimental results show that the method based on the analysis of the normalized mutual information is effective to delete redundancy images and greatly reduces diagnosis time.

Diffusion Tensor Imaging (DTI) has received increasing attention in the neuroimaging community. However, the complex Diffusion Weighted Images (DWI) acquisition protocol are prone to artifacts induced by motion and low signal-to-noise ratios (SNRs). A rigorous quality control (QC) and error correction procedure is absolutely necessary for DTI data analysis. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts. We propose a new regional, alignment-independent DTI-QC measure that is based in the DTI domain employing the entropy of the regional distribution of the principal directions. This new QC measurement is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Experiments show that our automatic method can reliably detect and potentially correct such residual artifacts. The results indicate its usefulness for general quality assessment in DTI studies.

DICOM(Digital Imaging and Communications in Medicine) is a kind of digital imaging and communications standard in medicine,which covers medical image collection,filing,communication,display,query and almost all information exchange protocols.It simplifies the implementation of medical image information exchange.This paper proposes a method of extracting information,displaying and processing of DICOM images based on Qt and DCMTK,and preliminarily realizes a DICOM imaging browser.

Discrimination of abnormal images from the numerous wireless capsule endoscope (WCE) video sequence images is laborious and time-consuming, so that a computer-based automatic image recognition system is desired for this task. We propose an algorithm to allow feature extraction from each image channel and decision fusion using multiple BP neural networks. The algorithm was tested and the results demonstrated its high efficiency and accuracy in identification of abnormalities in the WCE images.