磁共振成像设备图像均匀性及层厚检测方法分析与评价

图像均匀性及层厚是磁共振成像设备进行质量控制的主要性能参数.文章利用美国体膜实验室生产的Magphan SMR100体膜对磁共振成像设备的图像均匀性及层厚进行计量检测,分析了影响图像均匀性的各种因素以及图像不均匀对层厚测量带来的偏差,从而提高对磁共振成像设备周期检定的实践水平,确保磁共振成像设备图像清晰、可靠.     

单张纸平版印刷机压力均匀性与稳定性的测量方法

平版印刷机压力均匀性与稳定性直接影响印刷质量,是印刷机的重要评价指标.文章叙述了平版印刷机(胶印机)印力均匀性与稳定的测量方法,举例说明了测量过程,并对测量数据进行了不确定度评定.     

薄壁回转曲面壁厚的测量方法

介绍一种公差尺寸小薄壁且为回转曲面工件壁厚测量的方法,并对该测量方法进行了不确定度分析。该测量方法具有不确定度高、易于操作、适用于大批量测量的特点。     

等厚干涉数字处理系统研究与测量

将CCD和计算机图像处理技术融入到等厚干涉仪测量系统中,实现测量过程自动化,数据处理微机化,降低劳动强度,提高测量效率和测量准确定。     

齿厚卡尺综合误差测量结果的不确定度评定

文章介绍了齿厚卡尺综合误差测量结果不确定度的评定方法,解决了校准规范中没有不确定度分析的问题。     

恒温槽温度均匀性测量值的不确定度评定

本文介绍恒温槽温度均匀性测量值的不确定度评定方法。     

高精度平面度测量方法的研究

本以中等尺寸０００级平板为研究对象,采用双频激光测量系统的直线度测量部件进行平面度测量,分析计算了这一测量方法的测量不确定度,并进行了实际测试。     

小管径壁厚超声测量方法及测量不确定度分析

导管等管状类零件是飞机、发动机等产品的重要构件,这类构件承担着输送燃油、液压、氧气、冷气等任务,处于承压工作状态,需要保证零件的强度,才能保证产品的使用安全,壁厚作为直接影响强度的指标,通常对于其最小厚度有着严格的控制要求。但是常用的超声测厚在实际的小管径厚度现场测量时会出现测量数据不稳定甚至无法显示厚度数据的现象,导致超声测厚数据无法作为厚度评价依据。论文针对小管径管状类零件壁厚超声测量难题,通过对超声测厚机理的分析,提出频率、换能器直径、延迟块外形、换能器夹持工装等方面的改进建议,从而获得稳定有效的测量数据。同时对改进后的超声测厚提出基于蒙特卡洛方法的测厚不确定度分析方法,并通过试件进行测量不确定度的分析、评定,供从事小管径壁厚超声测量的检测人员参考。     

化学镀厚层Ni-P合金工艺的研究及应用

本文系化学镀厚层Ni-P合金工艺的研究及其应用.已配制2300升镀液,成功地镀复长1900mm、外径600mm的大型复杂工件.镀液较稳定,沉积速度较快.能获得厚度超过60μm,未经热处理HV_(200)达600左右,平均含磷量为6.36%,孔隙率少,结合力良好的银白色光泽镀层.     

气相色谱-质谱联用仪校准中常见故障问题分析及仪器日常维护

随着当今科技的飞速发展,气相色谱-质谱联用仪（以下简称GC/MS）的应用范围越来越广泛,在食品安全、生物医药、石油及石油化工产品及生态环境的保护等领域得到广泛的应用,究其原因是缘于GC/MS具有定性专属性强、灵敏度高、检测速度快的优势[1]。目前GC/MS虽已广泛应用,但在日常使用中难免会出现一些问题,文章通过介绍GC/MS现场校准过程中常见故障问题及排查解决办法,以及GC/MS的日常使用与维护,进而提高其使用价值。     

Analytical methods for characterizing magnetic resonance probes

The efficiency of Gd(III) contrast agents in magnetic resonance image enhancement is governed by a set of tunable structural parameters. Understanding and measuring these parameters requires specific analytical techniques. This Feature describes strategies to optimize each of the critical Gd(III) relaxation parameters for molecular imaging applications and the methods employed for their evaluation.     

Adrenal tumor characterization on magnetic resonance images

Adrenal tumors occur on adrenal glands and are generally detected on abdominal area scans. Adrenal tumors, which are incidentally detected, release vital hormones. These types of tumors that can be malignant affect body metabolism. Both of benign and malign adrenal tumors can have a similar size, intensity, and shape, this situation may lead to wrong decision during diagnosis and characterization of tumors. Thus, biopsy is done to confirm diagnosis of tumor types. In this study, adrenal tumor characterization is handled by using magnetic resonance images. In this way, it is wanted that patient can be disentangled from one or more imaging modalities (some of them can includes X-ray) and biopsy. An adrenal tumor image set, which includes five types of adrenal tumors and has 112 benign tumors and 10 malign tumors, was used in this study. Two data sets were created from the adrenal tumor image set by manually/semiautomatically segmented adrenal tumors and feature sets of these data sets are constituted by different methods. Two-dimensional gray-level co-occurrence matrix (2D-GLCM), gray-level run-length matrix (GLRLM), and two-dimensional discrete wavelet transform (2D-DWT) methods were analyzed to reveal the most effective features on adrenal tumor characterization. Feature sets were classified in two ways: benign/malign (binary classification) and type characterization (multiclass classification). Support vector machine and artificial neural network classified feature sets. The best performance on benign/malign classification was obtained by the 2D-GLCM feature set. The best results were assessed with sensitivity, specificity, accuracy, precision, and F-score metrics and they were 99.17%, 90%, 98.4%, 99.17%, and 99.13%, respectively. The highest classification performance on type characterization was obtained by the 2D-DWT feature set as 59.62%, 96.17%, 93.19%, 54.69%, and 54.94% for sensitivity, specificity, accuracy, precision, and F-score metrics, respectively.     

Optoelectronic parallel watershed implementation for segmentation of magnetic resonance brain images

An optoelectronic implementation for the morphological watershed transform is proposed. Fiber-optic programmable logic arrays are used in the implementation because of their high fan factors at high clock speeds. Image segmentation is one of the main applications of the watershed transform. Based on the optoelectronic implementation, an algorithm for the segmentation of axial magnetic resonance (MR) head images to extract information on brain matter is presented. Simulation results for the different steps of the segmentation process are presented.     

A fast and robust segmentation of magnetic resonance brain images using a combination of the pyramidal approach and level set method

We propose in this article an approach to optimize the processing time and to improve the quality of brain magnetic resonance images segmentation. Level set method (LSM) was adopted with a periodic reinitialization process to prevent the LS function from being too steep or too flat near the interface. Although it is used to maintain the stability of the interface evolution and gives interesting results, it requires a longer processing time. To overcome this disadvantage and reduce the processing time, we propose a hybridization with a regular Gaussian pyramid, which reduces the resolution of the initial image and prevents the possibility of local minima. To compare the different segmentation algorithms, we used six types of quality measurements: specificity, sensitivity, Dice similarity, the Jaccard index, and the correctly and incorrectly marked pixels. A comparison between the results obtained by LSM, LSM with reinitialization, the approach of Barman et al., An International Journal 1 (2011), particle swarm optimization based on the Chan and Vese model (Mandal et al., Engineering Applications of Artificial Intelligence 35 (2014), 199‐214) and by our hybrid approach reveals a clear efficiency of our hybridization strategy. The processing time was significantly reduced, and the quality of segmentation was improved. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 243–253, 2016     

Classification of magnetic resonance images from rabbit renal perfusion

The feasibility of using chemometric techniques for the automatic detection of whether a rabbit kidney is pathological or not is studied. Sequential images of the kidney are acquired using Dynamic Contrast-Enhanced Magnetic Resonance Imaging with contrast agent injection. A segmentation approach based upon principal component analysis (PCA) is used to separate out the cortex from the rest of the kidney including the medulla, the renal pelvic, and the background. Two classifiers (Soft Independent Method of Class Analogy, SIMCA; Partial Least Squares Discriminant Analysis, PLS-DA) are tested for various types of data pre-treatment including segmentation, feature extraction, centering, autoscaling, standard normal variate transformation, Savitsky-Golay smoothing, and normalization. It is shown that (i) the renal cortex contains more discriminating information on kidney perfusion changes than the whole kidney, and (ii) the PLS-DA classifiers outperform the SIMCA classifiers. PLS-DA, preceded by an automated PCA-based segmentation of kidney anatomical regions, correctly classified all kidneys and constitutes a classification tool of the renal function that can be useful for the clinical diagnosis of renovascular diseases.     

Neural network approach to segmentation of magnetic resonance head images

A novel image segmentation scheme based on a neural network has been implemented to segment magnetic resonance head images. A three-layer perceptron-type neural network, trained with backward error propagation algorithm was used. The scheme utilizes first-echo intensity and computed T₂ values to construct a two-parameter space for classification. After training on a selected slice, the method successfully segments all slices for a given subject without any further human interaction.     

Multiclassifier for severity-level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional Jaya optimizer-deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively.