Image Processing of Biological Liquids Films for Medical Diagnostics

In this paper, the development of smart medical autonomous technology is considered. An example of a smart medical autonomous distributed system for diagnostics is also discussed. To develop this system for medical image analysis we review several processing methods. The use of the cuneiform dehydration method for medical diagnosis is considered. The experimental results obtained for blood serum dehydrated films are presented. The author proposes an algorithm for the primary identification of structures formed in the films and their use for automated detection of various structures for diagnostic purposes. The paper describes the first stage of image processing, i.e. the selection of filtering types for the correct identification of structural features and characteristics of the images. The results of filtering and some computational results of various types of structures in the films are presented.

Image processing algorithm acceleration using reconfigurable macro processor model

The concept and advantage of reconfigurable technology is introduced. A kind of processor architecture of re configurable macro processor (RMP) model based on FPGA array and DSP is put forward and has been implemented. Two image algorithms are developed: template-based automatic target recognition and zone labeling. One is estimating for motion direction in the infrared image background, another is line picking-up algorithm based on image zone labeling and phase grouping technique. It is a kind of 'hardware' function that can be called by the DSP in high-level algorithm. It is also a kind of hardware algorithm of the DSP. The results of experiments show the reconfigurable computing technology based on RMP is an ideal accelerating means to deal with the high-speed image processing tasks. High real time performance is obtained in our two applications on RMP.

3D surface profile diagnosis using digital image processing for laboratory use

The measurement of the surface quality and the profile preciseness is major issues in many industrial branches such that the surface quality of semi products directly affects the subsequent production steps.Although,there are many ways to obtain required data,the hardware necessary for the measurements such as 2D or 3D scanners,depending on the problem’s complexity,is too expensive.Therefore,in this paper,what we put forward as a novelty is an algorithm which is verified on the model of simple 3D scanner on the image processing basis with the resolution of 0.1 mm.There are many ways to scan surface profile;however,the image processing currently is the most trending topic in industry automation.Most importantly,in order to obtain surface images,standard high resolution reflex camera is used and thus the post processing could be realized with MatLab as the software environment.Therefore,this solution is an alternative to the expensive scanners,and single-purpose devices could be extended by many additional functions.

Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size.At each step,the SVD is determined.First,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or not.Finally,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination variation.It is compared with other similarmethods and presents very promised results.

浅谈PET-CT技术及其在临床的应用

近些年,影像诊断学的一个重要进展,就是图像融合技术的发展与应用,一项将形态与功能互补结合的先进医学影像技术快速发展,这就是PET-CT技术,PET-CT作为放射影像医学检查中的最新技术,在临床使用上将得到广大的认同。

Image-Pro Plus图像处理技术对碎石封层覆盖率的检测方法研究

本文首先对Image-Pro Plus(IPP)图像处理技术进行了介绍,并采用IPP图像处理技术对碎石封层表面露石情况进行了检测,得到了碎石封层覆盖率拟合公式。对比IPP图像处理方法的检测结果与碎石封层覆盖率常规现场检测结果可知,该拟合公式准确可靠。IPP图像处理技术可以作为一种准确且快速的检测碎石封层的覆盖率方法,研究成果对提高开普封层的施工质量意义重大。

The Symbiotic Relationship Unraveling the Interplay between Technology and Artificial Intelligence(An Intelligent Dynamic Relationship)

This article investigates the dynamic relationship between technology and AI(artificial intelligence)and the role that societal requirements play in pushing AI research and adoption.Technology has advanced dramatically throughout the years,providing the groundwork for the rise of AI.AI systems have achieved incredible feats in various disciplines thanks to advancements in computer power,data availability,and complex algorithms.On the other hand,society’s needs for efficiency,enhanced healthcare,environmental sustainability,and personalized experiences have worked as powerful accelerators for AI’s progress.This article digs into how technology empowers AI and how societal needs dictate its progress,emphasizing their symbiotic relationship.The findings underline the significance of responsible AI research,which considers both technological prowess and ethical issues,to ensure that AI continues to serve the greater good.

Deep learning-based on-line image analysis for continuous industrial crystallization processes

In situ microscopic imaging is a useful tool in monitoring crystallization processes,including crystal nucleation,growth,aggregation and breakage,as well as possible polymorphic transition.To convert the qualitative information to be quantitative for the purpose of process optimization and control,accurate analysis of crystal images is essential.However,the accuracy of image segmentation with traditional methods is largely affected by many factors,including solid concentration and image quality.In this study,the deep learning technique using mask region-based convolutional neural network(Mask R-CNN)is investigated for the analysis of on-line images from an industrial crystallizer of 10 m^(3) operated in continuous mode with high solid concentration and overlapped particles.With detailed label points for each crystal and transfer learning technique,two models trained with 70,908 and 7,709 crystals respectively are compared for the effect of training data amount.The former model effectively segments the aggregated and overlapped crystals even at high solid concentrations.Moreover,it performs much better than the latter one and traditional multi-scale method both in terms of precision and recall,revealing the importance of large number of crystals in deep learning.Some geometrical characteristics of segmented crystals are also analyzed,involving equivalent diameter,circularity,and aspect ratio.

Application of Computer Vision Technology in Agriculture

With the development of image processing technology and computer, computer vision technology has been widely used in the production of agriculture,and has made many important achievements. This paper reviews its-research progress on diagnosis of agricultural products, water diagnosis, weed identification,product quality testing and grading, agricultural picking and sorting and other as- pects, and finally put forward its existing problems and prospects for the future.

Performance of common imaging techniques vs serum biomarkers in assessing fibrosis in patients with chronic hepatitis B: A systematic review and meta-analysis

BACKGROUND Noninvasive biomarkers have been developed to predict hepatitis B virus(HBV)related fibrosis owing to the significant limitations of liver biopsy.Both serum biomarkers and imaging techniques have shown promising results and may improve the evaluation of liver fibrosis.However,most of the previous studies focused on the diagnostic effects of various imaging techniques on fibrosis in all chronic liver diseases.AIM To compare the performance of common imaging methods and serum biomarkers for prediction of significant fibrosis caused only by HBV infection.METHODS A systematic review was conducted on the records available in PubMed,EMBASE,and the Cochrane Library electronic databases until December 2018.We systematically assessed the effectiveness of two serum biomarkers and three imagine techniques in predicting significant fibrosis solely caused by HBV infection.The serum biomarkers included aspartate aminotransferase-to-platelet ratio index(APRI)and fibrosis index based on the 4 factors(FIB-4).The three imaging techniques included acoustic radiation force impulse(ARFI),FibroScan,and magnetic resonance elastography(MRE).Three parameters,the area under the summary receiver operating characteristic curve(AUSROC),the summary diagnostic odds ratio,and the summary sensitivity and specificity,were used to examine the accuracy of all tests for liver fibrosis.RESULTS Out of 2831 articles evaluated for eligibility,204 satisfied the predetermined inclusion criteria for this current meta-analysis.Eventually,our final data contained 81 studies.The AUSROCs of serum biomarkers of APRI and FIB-4 were both 0.75.For imaging techniques(ARFI,FibroScan,and MRE),the areas were 0.89,0.83,and 0.97,respectively.The heterogeneities of ARFI and FibroScan were statistically significant(I2>50%).The publication bias was not observed in any of the serum biomarkers or imaging methods.CONCLUSION These five methods have attained an acceptable level of diagnostic accuracy.Imaging techniques,MRE in particular,demonstrate significant advantages in accurately predicting HBV-related significant fibrosis,while serum biomarkers are admissible methods.

Primary biliary cirrhosis degree assessment by acoustic radiation force impulse imaging and hepatic fibrosis indicators

AIM: To evaluate the assessment of primary biliary cirrhosis degree by acoustic radiation force impulse imaging (ARFI) and hepatic fibrosis indicators. METHODS: One hundred and twenty patients who developed liver cirrhosis secondary to primary biliary cirrhosis were selected as the observation group, with the degree of patient liver cirrhosis graded by Child-Pugh (CP) score. Sixty healthy individuals were selected as the control group. The four indicators of hepatic fibrosis were detected in all research objects, including hyaluronic acid (HA), laminin (LN), type III collagen (PC III), and type IV collagen (IV-C). The liver parenchyma hardness value (LS) was then measured by ARFI technique. LS and the four indicators of liver fibrosis (HA, LN, PC III, and IV-C) were observed in different grade CP scores. The diagnostic value of LS and the four indicators of liver fibrosis in determining liver cirrhosis degree with PBC, whether used alone or in combination, were analyzed by receiver operating characteristic (ROC) curve. RESULTS: LS and the four indicators of liver fibrosis within the three classes (A, B, and C) of CP scores in the observation group were higher than in the control group, with C class > B class > A class; the differences were statistically significant (P < 0.01). Although AUC values of LS within the three classes of CP scores were higher than in the four indicators of liver fibrosis, sensitivity and specificity were unstable. The ROC curves of LS combined with the four indicators of liver fibrosis revealed that: AUC and sensitivity in all indicators combined in the A class of CP score were higher than in LS alone, albeit with slightly decreased specificity; AUC and specificity in all indicators combined in the B class of CP score were higher than in LS alone, with unchanged sensitivity; AUC values (0.967), sensitivity (97.4%), and specificity (90%) of all indicators combined in the C class of CP score were higher than in LS alone (0.936, 92.1%, 83.3%). CONCLUSION: The diagnostic value of PBC cirrhosis degree in liver cirrhosis degree assessment by ARFI combined with the four indicators of serum liver fibrosis is of satisfactory effectiveness and has important clinical application value.

Research on Manufacturing Technology Based on Machine Vision

The concept of machine vision based manufacturing technology is proposed first,and the key algorithms used in two-dimensional and three-dimensional machining are discussed in detail.Machining information can be derived from the binary images and gray picture after processing and transforming the picture.Contour and the parallel cutting method about two-dimen- sional machining are proposed.Polygon approximating algorithm is used to cutting the profile of the workpiece.Fill Scanning al- gorithm used to machining inner part of a pocket.The improved Shape From Shading method with adaptive pre-processing is adopted to reconstruct the three-dimensional model.Layer cutting method is adopted for three-dimensional machining.The tool path is then gotten from the model,and NC code is formed subsequently.The model can be machined conveniently by the lathe, milling machine or engraver.Some examples are given to demonstrate the results of lmageCAM system,which is developed by the author to implement the algorithms previously mentioned.

Status and prospects of frozen soil studies using CT technology

This paper introduces the characteristics of Computed Tomography （CT） technology and reviews its history, current situation, representative achievements, and use of using CT technology on frozen soil study, including auxiliary equipment specially de- signed for frozen soil studies. CT numbers are used to analyze frozen soil internal structure change, defining and exploring dam- age evolution, and use of CT images on observing soil mesostructure. Finally, this paper presents existing problems confronted by using CT in frozen soil studies, possible solutions and challenges, among which, we introduce high quality CT image processing for frozen soils, and relations between CT number change and each component change on frozen soil samples within the region of interest. It is shown that present CT technology is one of the most ideal and effective technology to study frozen soil mesostructure using non-desmactive testing. CT technology will play a key role in the study and development in the field of frozen soil by means of auxiliary equipment and the digital imaging processing.

Study progression in application of process analytical technologies on film coating

Film coating is an important unit operation to produce solid dosage forms,thereby,the monitoring of this process is helpful to find problems in time and improve the quality of coated products.Traditional methods adopted to monitor this process include measurement of coating weight gain,performance of disintegration and dissolution test,etc.However,not only do these methods cause destruction to the samples,but also consume time and energy.There have recently emerged the applications of process analytical technologies(PAT)on film coating,especially some novel spectroscopic and imaging technologies,which have the potential to real-time track the progress in film coating and optimize production efficiency.This article gives an overview on the application of such technologies for film coating,with the goal to provide a reference for the further researches.

基于机器视觉的飞机故障检查系统

针对当前飞机维修检查工作以人工目视检查为主、效率低且存在人为因素影响的情况,设计一套基于图像识别与机器深度学习的飞机部件表面无损检测系统。收集并整理了某航空公司一线飞机维修员拍摄的飞机机身及发动机部件图片,对图片集进行预处理,包括通道提取、Sobel滤波处理及二值化;最后用Blob分析对处理后的图像进行特征提取与系统分析。系统运行速度快、准确率高且可连续自动识别图像。利用机器视觉技术对飞机部件表面进行无损检测不仅可以提高生产效率,同时可以去除人为因素对航空器飞行安全的影响,使得飞机的飞行安全得到进一步提升。实践证明,该系统性能稳定可靠,具有极高的推广应用价值。

计算机图像处理技术在网页设计中的应用

运用文献研究法、综合分析法,在明确计算机图像处理技术概念及其相关技术类型基础上,阐述网页设计中计算机图像处理技术应用的优势,从网页类型与图像格式搭配设计等角度,提出几点如何发挥计算机图像处理技术在网页设计中应用优势作用的思路与策略.计算机图像处理技术多样性和图像组合优势可有效拓展网页设计思路,使其更加新颖且符合新时代下用户审美取向,从而促进网页的良好发展.

视频侦查技术关键及其发展展望

视频侦查技术的核心是通过视频图像的提取、查看、分析和研判来获取侦查线索、固定涉案证据。多年来经过在实战应用中不断地发展创新,视频侦查技术形成了自己独特的关键技术体系:视频信息分析解读技术是侦查应用和证据固定的基础和核心;视频证据固定保全技术的规范是审判中心主义的客观要求,可以获取视频侦查记录报告、视频检验鉴定报告或视频数据关联报告;低质量视频图像的增强恢复技术专业性强,应用范围窄,技术成熟度高,然而它不断面临新的挑战。目前,视频数据的智能应用在大数据背景下变得越来越重要,仍需进一步突破视频自动识别技术的应用范畴,建立完善多层次的视频数据综合应用体系,打造适应不同业务需要的视频数据实战应用模型。

背部痧象特征要素数显划分方法

目的通过对痧象图片数据的研究,提出一种数显的客观方法来对背部痧象特征要素进行准确辨识,以避免主观认知不同带来的偏差,帮助人们快速辨识痧象的相关特征,为中医医师的诊断提供一定的客观依据,减少主观化成分。方法首先,采用深度语义分割算法模型对采集的背部痧象图片进行分割,以获取只包含痧象的部分区域,去除图片中其他无关部分,尽可能减少对后续实验所带来的影响。然后针对分割得到的图像,结合中医五脏六腑在背部的映射区,采用关键区域检测的方法对痧象颜色特征进行3种区间的识别;根据中医主观上对于痧象形状的划分,采用像素统计方法将痧象形状划分为片状和点状两种。最后,通过准确度、精确度、召回率、F1值对客观识别的结果进行数据上的评价。结果本文提出的客观数显方法在痧象颜色特征划分上的准确率达到了80.56%,在痧象形状特征的划分上准确率达到了89.60%,能够较为准确地对痧象特征要素进行划分,具有一定的可行性。结论本文提出的数显方法能够对痧象的颜色和形状特征进行较为准确的辨识,可以在很大程度上避免主观认知所造成的辨识不同问题,在辅助医师诊断以及用于中医痧象特征的识别教学方面具有较大的应用前景,可为正确了解痧象特征信息提供帮助。

指针式仪表读数识别的研究现状与发展

在高温高压强磁场等恶劣环境下,指针式仪表有着优于数显式仪表的良好性能表现,对指针式仪表读数识别进行研究具有重要的现实意义。近年来,深度学习与计算机视觉紧密结合,基于深度学习的指针式仪表读数识别技术是前沿研究的重点方向。文中首先概述指针式仪表读数识别的过程,然后从3个方面论述指针式仪表读数识别技术的研究现状与进展:仪表图像的预处理、仪表表盘区域的检测和仪表的读数识别,论述过程中分别阐述基于传统机器学习的方法和基于深度学习的方法。最后介绍公开的仪表数据集和应用场景,并从深度学习算法、巡检机器人工作特点和公共数据集的建立3个方面对未来的研究进行展望与建议。

超厚纸张表面缺陷智能化图像处理与识别系统设计研究

设计一套智能化图像处理与识别系统,用于超厚纸张表面缺陷的检测,以解决传统超厚纸检测成本高、效率低、错误率高、精度低等问题。通过综合分析图像处理技术在超厚纸张表面缺陷检测中的可行性,从图像处理技术流程、应用可行性和系统设计等角度展开讨论,提出基于机器视觉的超厚纸张表面缺陷检测系统设计方案,包括整体设计、硬件环境和软件平台的选择与搭建,期望该系统能够高效、准确地检测超厚纸张表面的各类缺陷,提高超厚纸张生产质量和效率。