Image Processing Services Research Articles

Performance Analysis of Compression Algorithms on Matrix Data: Data Transfer Optimization in Microservices Architectures

With the rapid proliferation of microservices architectures these days, the efficient and fast transfer of large matrix data between services has become a significant challenge. This study presents an analysis aimed at finding solutions to this challenge. The analysis addresses the compression and decompression of large matrix data, focusing on lossless compression algorithms to optimize data transfer without data loss. The study is implemented on an example scenario. This scenario is taken from a project with a microservice architecture. In the example scenario, an image processing service developed in Python programming language generates 640x480 matrix data. After going through a compression algorithm, this data is periodically transferred to a backend service developed in C# programming language. This data is then stored in a database. In the final stage, decompression operations are performed so that this data can be used for reporting. The performance of various compression algorithms in the data compression, database storage and report generation stages is extensively tested. Within the scope of the study, tests were performed using five different compression algorithms (Gzip, Zlib, Deflate, Brotli and Bz2). The results are obtained through performance tests aimed at determining the most optimized end-to-end solution. Analyzing the performance of the compression algorithms on the example scenario, the Brotli algorithm gives the most optimal result in terms of both speed and compression size. This work makes an important contribution to data transfer optimization in microservice architectures and provides a reference for research in this area by presenting the performance analysis of various compression algorithms.

Implementasi Google Cloud Vision untuk Deteksi Ketersediaan Lahan Parkir Kendaraan Mobil di Perguruan Tinggi

The increasing number of car users in colleges has posed significant challenges in effectively managing parking lots. This research focuses on the implementation of Google Cloud Vision to detect and monitor the availability of car parking lots in a college environment. By utilizing cloud-based image processing services and machine learning algorithms from Google Cloud Vision, the proposed system aims to provide real-time analysis and reporting on parking slot availability. The methodology used involves capturing images of the parking area using strategically placed cameras, then uploading these images to Google Cloud Vision to detect the presence or absence of cars. The system is designed to be scalable, ensuring that it can handle different sizes and complexities of parking areas. Preliminary results show that the approach using Google Cloud Vision offers a high level of accuracy in identifying occupied and empty parking slots, thus providing a reliable tool for parking management in a college environment. This work discusses the development, implementation, and evaluation of the system, and highlights its potential in improving parking lot utilization efficiency and reducing the time users spend searching for available parking spaces.

РОЗВИТОК КОМПЕТЕНТНОСТІ З ВЕБ-ДИЗАЙНУ ПЕДАГОГІЧНИХ ПРАЦІВНИКІВ ЗАКЛАДІВ ПРОФЕСІЙНОЇ (ПРОФЕСІЙНО-ТЕХНІЧНОЇ) ОСВІТИ

Relevance: in modern conditions, teachers are actively involved in the creation of electronic textbooks, interactive notebooks, various forms and test tasks, etc. All this means developing and designing objects of the Internet information environment from the standpoint of user-friendliness and meeting the user's aesthetic needs. The web design competence development of teaching staff at vocational education institutions is an urgent need in the context of digitalisation of education. Aim: defining the structure of competence in web design for teaching staff of vocational education institutions and substantiating the methodology for its development. Methods: analysis and synthesis – to determine the development state of the problem under study and the list of competence in web design of teaching staff; generalization – to formulate conclusions and recommendations for the web design competence development of teaching staff at vocational education institutions; diagnostic (testing, conversation) – to find out the level of competence in web design of teaching staff. Results: it was found that more than 17% of teachers design and create websites and web pages that combine text with sounds, pictures, graphics and video; 5-7% use various graphic editors and image processing services; about 80 % of respondents carry out independent design development of electronic publications (a product containing information in electronic form); less than 6 % of respondents indicated that there is a unified approach to the design of educational materials on hard copy and for posting on the Internet. The structure of competence in web design for teaching staff of vocational education institutions consists of technical skills (basics of composition, typography, colour science, graphic design, design software, user interface (UI), user experience (UX), responsive design) and soft skills (project management, time management, copywriting, web design awareness, creative thinking, critical thinking, attention to detail, communication, self-reflection). To assess this competence, four levels (initial, low, medium, high) have been developed, taking into account the indicators of motivational, cognitive, activity and reflective criteria. Taking into account the specifics of the teaching staff activities at vocational education institutions, a methodology for their design education has been developed, which is aimed at developing competence in web design and includes seven stages. Conclusions: Teaching staff of vocational education institutions are involved in the development of e-learning content, but have a low level of competence in web design. The web design competence of a teaching staff member of a vocational education institution is a combination of knowledge, skills and attitudes to web design technologies to create educational content for performing tasks, solving problems, communicating, managing information, working together in the information and educational environment of a vocational education institution, doing it effectively, appropriately, safely, critically, creatively, independently and ethically. At the same time, the teacher does not have to be a web designer, he/she uses the tools and ready-made algorithms of the services he/she works with, the basis of his/her training in this field is graphic design, typography, copywriting, etc. for the effective presentation of electronic content.

Automatic Computer Propped Diagnosis Framework of Liver Cancer Detection using CNN LSTM

Liver cancer detection using the computer vision methods and machine learning already received significant attention of researchers for authentic diagnosis and on-time medical attentions. The Computer Aided Diagnosis (CAD) preferred for cancer detection all over the world which is based on image processing service. Earlier CAD tools were designed using conventional machine learning techniue using semi-automatic approach. The modern growth of deep learning for automatic detection and classification leads to significant improvement in accuracy. This paper the automatic CAD framework for liver cancer detection using Convolutional Neural Network (CNN) including Long Short Term Memory (LSTM). The input Computed Tomography (CT) scan images early pre-processed for quality enhancement. After that we applied the lightweight and accuracy field of Interest (ROI) extraction technique using dynamic binary segmentation. From ROI images, we extracted automated CNN-based appearance and hand-craft features. The consolidation of both features formed unique feature set for classification purpose. The LSTM block is then achieve the classification either into normal or diseased CT image. The CNN-LSTM model is designed in this paper to complement the accuracy of liver cancer detection compared to other deep learning solutions. The experimental results of proposed design using CNN-based features and hybrid hand craft features outperformed the recent state-of-art methods.

Real-time Vehicle Surveillance System Based on Image Processing and Short Message Service

This paper proposes a real-time vehicle surveillance system based on image processing approach tailored with short message service. A background subtraction, color balancing, chain code based shape detection, and blob filtering are used to detect suspicious moving human around the parked vehicle. Once detected, the developed system will generate a warning notification to the owner by sending a short message to his mobile phone. The current frame of video image will also be stored and be sent to the owner e-mail for further checking and investigation. Last stored image will be displayed in a centralized monitoring website, where the status of the vehicle also can be monitored at the same time. When necessary, the stored images can be used during investigation process to assist the authority to take further legal actions.

Night Motion Detection and Alert System

Closed-Circuit TV (CCTV) system is one of the technologies in the surveillance field to solve the problem of detection and monitoring by providing extra features such as email alert or motion detection. However, detecting and alerting the admin on CCTV system may complicate due to the complexity to integrate the main program with an external Application Programming Interface (API). In this study, pixel processing algorithm is applied due to its efficiency and SMS alert is added as an alternative solution for users who opted out email alert system or have no Internet connection. A CCTV system with SMS alert (CMDSA) was developed using evolutionary prototyping methodology. The system interface was implemented using Microsoft Visual Studio while the backend components, which are database and coding, were implemented on SQLite database and C# programming language, respectively. The main modules of CMDSA are motion detection, capturing and saving video, image processing and Short Message Service (SMS) alert functions. Subsequently, the system is able to reduce the processing time making the detection process become faster, reduce the space and memory used to run the program and alerting the system admin instantly.

Computer Aided Diagnosis Based on Artificial Intelligence in Providing Support for Health Services in Rural Areas

The health service system in Indonesia is still a homework that needs to be addressed by the government. Both in terms of equity, regulation, and integration between offline and online systems. As for what is happening now, there are at least some obstacles that need to be addressed and addressed as soon as possible. On the other hand, health development should beseen as an investment to improve the quality of human resources and support economic development, and have an essential role in poverty reduction efforts. Today’s primary health development problems include the high disparity in health status between socioeconomic levels, regions, and urban and rural areas. In general, the health status of people with high socioeconomic levels in western Indonesia and urban areas tends to be better. On the other hand, the health status of people with low socioeconomic status in eastern Indonesia and rural areas is still lagging. The quality, equity, and affordability of health services are also still low. The quality of service is an obstacle because medical personnel is minimal and equipment is inadequate. In terms of numbers, the ratio of health workers to the population to be served is still low. The affordability of services is closely related to the number and distribution of healthfacilities. In terms of the limitations of medical equipment, this is due to the high price of the medical equipment. Besides, special training is also needed in medical equipment, which training takes quite a long time, and the material is quite complex. With these conditions, it is necessary to optimize technology to develop medical equipment, especially diagnostic support equipment that is cheap, easy to use, and has high accuracy. Our research aims to address optimizing image processing technology and artificial intelligence in developing technology to support health services in rural areas. We tried to implement several cases of health services that often occur in rural areas, namely early detection of cataracts, optimizing the use of ultrasound machines, optimizing the use of lowquality MRI machines in the diagnosis of Alzheimer’s, using microscopic images to identify leukocytes and using X-ray images to support the diagnosis. COVID19. Of the several modulesthat we have developed for health care support technology, the results show promisingprospects with a good level of accuracy, ease to use, and use modalities that already exist in the lowest health service units to reduce prices. It is hoped that this image processing and artificial intelligence-based health service support module can be used in rural areas to overcome disparities in health services in Indonesia.

RAST GRAPHIC IMAGES PROCESSING AND FILTRATION SYSTEM SOFTWARE

Given the trend of active growth of graphic content on the World Wide Web and in the applied areas of practice of various industries, information systems and digital image processing services are becoming increasingly important, in particular, supporting appropriate operations for applying different types of filters to raster graphics. The aim of the article is to software development of a system for processing and filtering raster graphics for quick editing of files by content managers. Realization of the set purpose provides the decision of a number of problems: 1) the analysis of features of structure and construction of graphic vector images and modern graphic editors; 2) development and implementation of the class structure of the system; 3) description of the created system interface and the main functions for applying filters to the image. Scientific novelty. The article describes the possibilities of integrating a number of visual transformations and filtering with the support of creating convolution effects using the selected kernel matrix for raster images of equal level of detail and resolution. In conclusion, the article emphasizes that the processes of processing graphic bitmaps and adding appropriate filters to them are a priority for solving the application tasks of content managers of websites or groups of social networks. The advantages of using the developed system of processing and filtering of raster graphic images are its high speed, productivity of processing of parallel processing of a large number of images and portability, thanks to which the system can be used by different users without the need to install additional tools. The developed modular structure of the system allows to flexibly supplement or expand its functionality, in particular, quick integration of other data filtering algorithms into the basic software logic of the system is available.

CryptoLesion

The low-cost, accessing flexibility, agility, and mobility of cloud infrastructures have attracted medical organizations to store their high-resolution data in encrypted form. Besides storage, these infrastructures provide various image processing services for plain (non-encrypted) images. Meanwhile, the privacy and security of uploaded data depend upon the reliability of the service provider(s). The enforcement of laws towards privacy policies in health-care organizations, for not disclosing their patient’s sensitive and private medical information, restrict them to utilize these services. To address these privacy concerns for melanoma detection, we propose CryptoLesion , a privacy-preserving model for segmenting lesion region using whale optimization algorithm (WOA) over the cloud in the encrypted domain (ED). The user’s image is encrypted using a permutation ordered binary number system and a random stumble matrix. The task of segmentation is accomplished by dividing an encrypted image into a pre-defined number of clusters whose optimal centroids are obtained by WOA in ED, followed by the assignment of each pixel of an encrypted image to the unique centroid. The qualitative and quantitative analysis of CryptoLesion is evaluated over publicly available datasets provided in The International Skin Imaging Collaboration Challenges in 2016, 2017, 2018, and PH 2 dataset. The segmented results obtained by CryptoLesion are found to be comparable with the winners of respective challenges. CryptoLesion is proved to be secure from a probabilistic viewpoint and various cryptographic attacks. To the best of our knowledge, CryptoLesion is first moving towards the direction of lesion segmentation in ED.

Privacy-Preserving Krawtchouk Moment feature extraction over encrypted image data

Resource-constrained users outsource the massive image data to the cloud to reduce storage and computation overhead locally, but security and privacy concerns seriously hinder the applications of outsourced image processing services. Besides, existing image processing solutions in the encrypted domain still bring high computation overhead, and even lead to characteristic loss. To this end, we propose a Privacy-Preserving Krawtchouk Moment (PPKM) feature extraction framework over encrypted image data by utilizing the Paillier cryptosystem. First, a mathematical framework for PPKM implementation and image reconstruction is presented in the encrypted domain. Then, the detailed expanding factor and upper bound analysis shows that plaintext Krawtchouk moment and plaintext image reconstruction can be realized over encrypted image with PPKM. Furthermore, the computation complexity of PPKM can be significantly reduced with the block-based parallel algorithm. Experimental results verify that the PPKM is feasible and acceptable in practice in terms of image reconstruction capability and image recognition accuracy.

P5691Independent assessment if an image-processing service for the treatment of patients with ventricular tachycardias

Abstract Background/Introduction Substrate-based radiofrequency ablation (RFA) in combination with pre-procedural computed tomography (CT) or cardiac Magnetic Resonance Imaging (cMRI) emerged as a promising approach to treat ventricular tachycardias (VT). However, image-processing and 3D reconstruction of the relevant structures to embed them into a 3D electroanatomical mapping (EAM) system is time consuming and requires highly experienced personal and a dedicated software. Purpose The aim of the study was to present the first independent experience with a commercially available service of a internet platform in patients referred for RFA of VTs. Methods Seven consecutive patients (pts) with ischemic cardiomyopathy (ICM), non- ischemic cardiomyopathy (NICM) and dilated cardiomyopathy (DCM) referred for VT RFA underwent contrast-enhanced dual-energy CT. The anonymized DICOM dataset was uploaded to the internet platform. After processing by the specialists, the dataset was downloaded and exported in a format compatible with the 3D EAM System. The EAM was performed in sinus rhythm using a 3.5mm open-irrigated tip catheter or a magnetic remote 3.5mm open-irrigated tip catheter in combination with the remote magnetic navigation-system. A multipolar high-density mapping catheter was used in 6 pts. Scar was defined as bipolar voltage <0.5 mV, and scar border zone ≥0.5mV and <1.5 mV. Results of the internet platform-derived wall thinning (WT), scars and the defined substrate based on 3d EAM voltage maps were transferred into a 17-segment model, and the filling of every single segment was rated as 0%, 25%, 50%, 75% and 100%. For analysis, agreement of the filling (percentage) of the individual segments was quantified. Results Mean age was 67±8 year, BMI was 28±5 kg/m2 and 86% were males. File transfers and image processing was feasible in all patients. Agreement between the defined substrate (<0,5mV) and WT of 4mm was very good (≥90%) in 3 pts, good (≥75% & <90%) in one patient, moderate (≥50% & <75%) in one patient and poor (<50%) in one patient. Patient #1 #2 #3 #4 #5 #6 #7 Sex male male male female male male male Age 63y 56y 71y 65y 60y 69y 81y BMI 31kg/m2 32kg/m2 19kg/m2 25kg/m2 28kg/m2 34kg/m2 29kg/m2 LVEF 25% 60% 25% 25% 25% 31% 34% EDVI 123ml/m2 184ml/m2 69ml/m2 114ml/m2 142ml/m2 105ml/m2 75ml/m2 Catheter multipolar high-density + 3.5mm open-irrigated tip 3.5mm open-irrigated tip multipolar high-density + 3.5mm open-irrigated tip multipolar high-density + 3.5mm open-irrigated tip multipolar high-density + magnetic remote 3.5mm open-irrigated tip multipolar high-density + magnetic remote 3.5mm open-irrigated tip multipolar high-density + magnetic remote 3.5mm open-irrigated tip Quality 98% 96% 91% 66% 89% no match 53% Quality = Percentage match between defined substrate and WT. Superimpose – wall thinning and FAM Conclusion(s) Integration of substrate-based segmentation using the service of the internet platform is feasible in daily practice. Agreement between voltage-map based substrate definition and internet platform-based WT was satisfactory in the majority of patients.

Study on wind turbine blade defect detection system based on imaging array

Currently in the process of wind farm inspection, wind turbine blade appearance inspection mainly adopts the telescope or high-definition cameras, low detection efficiency, labor intensity and the precision is limited, in order to solve this problem, a kind of wind turbine blades defect recognition system based on image array is proposed. Through the joint of array camera and image processing server, the functions of the image acquisition, processing, and defect recognition and detection results output are implemented. The software of artificial intelligence deep learning based on neural network algorithm is used to identify the defects of blade image, and output quality analysis report, to realize automatic detection of wind turbine blade surface defect. The field measurement results show that the system greatly improves the efficiency and accuracy of wind turbine blade defect detection.

A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack

Recently, web application services based on cloud computing technologies are being offered. In the web-based application field of geo-spatial data management or processing, data processing services are produced or operated using various information communication technologies. Platform-as-a-Service (PaaS) is a type of cloud computing service model that provides a platform that allows service providers to implement, execute, and manage applications without the complexity of establishing and maintaining the lower-level infrastructure components, typically related to application development and launching. There are advantages, in terms of cost-effectiveness and service development expansion, of applying non-proprietary PaaS cloud computing. Nevertheless, there have not been many studies on the use of PaaS technologies to build geo-spatial application services. This study was based on open source PaaS technologies used in a geo-spatial image processing service, and it aimed to evaluate the performance of that service in relation to the Web Processing Service (WPS) 2.0 specification, based on the Open Geospatial Consortium (OGC) after a test application deployment using the configured service supported by a cloud environment. Using these components, the performance of an edge extraction algorithm on the test system in three cases, of 300, 500, and 700 threads, was assessed through a comparison test with another test system, in the same three cases, using Infrastructure-as-a-Service (IaaS) without Load Balancer-as-a-Service (LBaaS). According to the experiment results, in all the test cases of WPS execution considered in this study, the PaaS-based geo-spatial service had a greater performance and lower error rates than the IaaS-based cloud without LBaaS.

Image Feature Extraction in Encrypted Domain With Privacy-Preserving Hahn Moments

With outsourcing huge amount of images to cloud, it can reduce the local storage and computational overhead of resource-limited users, but the security concerns still impede the adoption of outsourced image processing services. Fortunately, image processing in the encrypted domain can handle this problem very well and has already attracted considerable attention due to its ability to protect valuable image information from being leaked to untrusted parties. However, the existing schemes incur high computational complexity, and none of them can be used as both global feature and local feature. To this end, we present a method to implement Hahn moments in the encrypted domain by using Somewhat Homomorphic Encryption (SHE) in this paper, named Privacy-Preserving Hahn Moments (PPHM). First, a mathematical framework is proposed to implement the PPHM and image reconstruction in the encrypted domain. Then, the detailed theoretical analysis about data expansion and quantization errors shows that plaintext Hahn moments and plaintext image reconstruction can be implemented by utilizing PPHM over encrypted image. Moreover, security analysis shows that the PPHM can guarantee the image content security. Theoretical analysis and experimental results demonstrate that the PPHM scheme can greatly reduce the computational complexity compared with the discrete cosine transform and the discrete wavelet transform in the encrypted domain. In addition, experimental results show that the PPHM has a better performance in both image reconstruction and image recognition. Furthermore, the suitable value of a scaling factor, which can provide guidance to users, is specified for image reconstruction and image recognition, respectively.

Smart cloud system with image processing server in diagnosing brain diseases dedicated for hospitals with limited resources.

The use of medical imaging in diagnosing brain disease is growing. The challenges are related to the big size of data and complexity of the image processing. High standard of hardware and software are demanded, which can only be provided in big hospitals. Our purpose was to provide a smart cloud system to help diagnosing brain diseases for hospital with limited infrastructure. The expertise of neurologists was first implanted in cloud server to conduct an automatic diagnosis in real time using image processing technique developed based on ITK library and web service. Users upload images through website and the result, in this case the size of tumor was sent back immediately. A specific image compression technique was developed for this purpose. The smart cloud system was able to measure the area and location of tumors, with average size of 19.91 ± 2.38 cm2 and an average response time 7.0 ± 0.3 s. The capability of the server decreased when multiple clients accessed the system simultaneously: 14 ± 0 s (5 parallel clients) and 27 ± 0.2 s (10 parallel clients). The cloud system was successfully developed to process and analyze medical images for diagnosing brain diseases in this case for tumor.

An Efficient Secret Key Homomorphic Encryption Used in Image Processing Service

Homomorphic encryption can protect user’s privacy when operating on user’s data in cloud computing. But it is not practical for wide using as the data and services types in cloud computing are diverse. Among these data types, digital image is an important personal data for users. There are also many image processing services in cloud computing. To protect user’s privacy in these services, this paper proposed a scheme using homomorphic encryption in image processing. Firstly, a secret key homomorphic encryption (IGHE) was constructed for encrypting image. IGHE can operate on encrypted floating numbers efficiently to adapt to the image processing service. Then, by translating the traditional image processing methods into the operations on encrypted pixels, the encrypted image can be processed homomorphically. That is, service can process the encrypted image directly, and the result after decryption is the same as processing the plain image. To illustrate our scheme, three common image processing instances were given in this paper. The experiments show that our scheme is secure, correct, and efficient enough to be used in practical image processing applications.

Cloud-based Satellite Image Processing Service by Open Source Stack: A KARI Case

Cloud-based Satellite Image Processing Service by Open Source Stack: A KARI Case

IMAGE cloud: medical image processing as a service for regional healthcare in a hybrid cloud environment

To handle the emergence of the regional healthcare ecosystem, physicians and surgeons in various departments and healthcare institutions must process medical images securely, conveniently, and efficiently, and must integrate them with electronic medical records (EMRs). In this manuscript, we propose a software as a service (SaaS) cloud called the iMAGE cloud. A three-layer hybrid cloud was created to provide medical image processing services in the smart city of Wuxi, China, in April 2015. In the first step, medical images and EMR data were received and integrated via the hybrid regional healthcare network. Then, traditional and advanced image processing functions were proposed and computed in a unified manner in the high-performance cloud units. Finally, the image processing results were delivered to regional users using the virtual desktop infrastructure (VDI) technology. Security infrastructure was also taken into consideration. Integrated information query and many advanced medical image processing functions-such as coronary extraction, pulmonary reconstruction, vascular extraction, intelligent detection of pulmonary nodules, image fusion, and 3D printing-were available to local physicians and surgeons in various departments and healthcare institutions. Implementation results indicate that the iMAGE cloud can provide convenient, efficient, compatible, and secure medical image processing services in regional healthcare networks. The iMAGE cloud has been proven to be valuable in applications in the regional healthcare system, and it could have a promising future in the healthcare system worldwide.

Medical Image Processing Server applied to Quality Control of Nuclear Medicine.

This paper is framed within the area of medical image processing and aims to present the process of installation, configuration and implementation of a processing server of medical images (MIPS) in the Fundación Escuela de Medicina Nuclear located in Mendoza, Argentina (FUESMEN). It has been developed in the Gabinete de Tecnologia Médica (GA.TE.ME), Facultad de Ingeniería-Universidad Nacional de San Juan. MIPS is a software that using the DICOM standard, can receive medical imaging studies of different modalities or viewing stations, then it executes algorithms and finally returns the results to other devices. To achieve the objectives previously mentioned, preliminary tests were conducted in the laboratory. More over, tools were remotely installed in clinical enviroment. The appropiate protocols for setting up and using them in different services were established once defined those suitable algorithms. Finally, it’s important to focus on the implementation and training that is provided in FUESMEN, using nuclear medicine quality control processes. Results on implementation are exposed in this work.

Performance-Aware Cloud Resource Allocation via Fitness-Enabled Auction

Cloud computing is a new computing paradigm which features renting the computation devices instead of buying them. In a typical cloud computing environment, there will always be different kinds of cloud resources and a number of cloud services making use of cloud resources to run on. As we can see, these cloud services usually have different performance traits. Some may be I/O-intensive, like those data querying services, while others might demand more CPU cycles, like 3D image processing services. Meanwhile, cloud resources also have different kinds of capabilities such as data processing, I/O throughput, 3D image rendering, etc. A simple fact is that allocating a suitable resource will greatly improve the performance of the cloud service, and make the cloud resource itself more efficient as well. In this paper, a new cloud resource allocating algorithm via fitness-enabled auction is proposed to guarantee the fitness of performance traits between cloud resources (sellers) and cloud services (buyers). We study the allocating algorithm in terms of economic efficiency and system performance, and experiments show that the allocation is far more efficient in comparison with the continuous double auction in which the idea of fitness is not introduced.