Centralized Cloud PACS-Free-Samples for Students-Myassignmenthelp

Question: How the Medical Images and Radiologists Connect to each other using Centralized Cloud PACS. Answer: Introduction The system that is mainly designated to support the medical image workflow can be termed as Private achieve and communication system (PACS). The PACS has mainly paired with the appearance of the digital acquisition device which have mainly lowered the upkeep and has improved the radiology department quality of service. PACS is simplistically commonly comprised of three major group of components; repositories, acquisition devices and viewer workstation. Despite the concept perfectly fits in the workflow of the radiology, these components can be stated as heterogeneous. Moreover, in the manufacturing sector specialist are there. Recently the concept of the cloud and the internet technology have been put together in deploying the concept of the PACS. The main aim of the report is to focus on the concept of medical image and radiologist and taking into consideration how it connects each other using the centralized cloud PACS. the main points which are discussed are the concept of the cloud computing and how it is applied to PACS, issue related to the migration to the cloud, the current situation which is being faced by the company and the sector of innovation and advantage. Background The DICOM protocol was introduced 2013. This was a major driving force for the adaptation of the Private achieve and communication system (PACS) [1]. The standard mainly enabled the factor of coexistence of vendors mainly defining how the image data of the medical image should be transmitted and stored with the PACS. Beside the actual data image, the DICOM image file are mainly composed of meta data section. This section mainly comprises of data related to the image such as clinical staff identification and patient details. both the image file and the message related to the network are composed by the DICOM objects which follows a concept of TLV (Tag, length and value). The value can directly be encoded in different formats as virtual representation (VR). The VR of an object mainly involve explicitly in the Structure of TLV or on the other hand simply defined in a dictionary [2]. The main result of the concept is that the repositories of medical image have started to be very much seen as a rich bank of data due to the factor that meta data held by the DICOM images mainly contain information which are important that are needed for the medical practice. Concept of cloud computing applied to PACS The concept can be referred to as a model of provisioning for the storage capacity and virtualized processing [7]. The storage system and the physical processor are mainly housed in large data centre and mainly managed by IT organisation. all the functionality based on the web service accessed with the help of internet. The concept of cloud computing may also be referred to as a term of exclusivity and privacy. Issue to migration to cloud The first challenge which is basically faced is what type of model is to be used. The choosing of the proper model which provides better delivery of the health care service which is given to the patient is a very issue. Generally, every health organisation contains a huge data base which mainly comprise of sensitive data as well as non-sensitive data. The sensitive data are those details which include X-RAY, CT/MRI scan and the patient medical report [1]. On the other hand, the non-sensitive data are those data such as patient bill and payment details. Recently the concept of the cloud and the internet are put together in the concept of the PACS. One of the most important issue is the retrieval of the image in this scenario. It can be stated that the issue which is related to the Private achieve and communication system is a very important factor [3]. Current situation The PACS system is mainly image management system and more difficult than the text database. The main abstract of the image alone is a very challenging sector. CBMIR (content based medical image retrieval mainly overcomes the shortcoming which is related to the traditional image retrieval system. This mainly provides retrieval method which is based on the colour, texture, space relation and other content features which are related to the medical image. CBMIR is a technology which can be used in order to achieve automatic reasoning process of the medical image and thus achieving computerised auxiliary diagnosis of any disease. Recently one of the focus point of the CBMIR research is to achieve content based medical image retrieval via the process of machine learning such as metric learning based on boosting framework [5]. Innovations and advantage Innovations and the advantage can be indicated by the following points. Based on the services which are related to the cloud, the image is mainly processed on the server side rather than the browser side. This makes full use of the powerful image processing capability of the server. This does not develop complex image algorithm based on processing but on the other hand also ensures the speed of the image and the efficiency related to processing. Compared to the traditional PACS which was based on C/S structure in the hospital, the system does not need to install the complex client. java applet or the flash plug in are not required. The system provides a scalability which is considerable good. It mainly deals with the image processing algorithm in a C++ project. Certain type of algorithm in this project is compiled and build into dynamic link library then the PHP middleware which can be used to implement certain processing which is related to the image [6]. Conclusion It can be concluded from the report that how the technology related to the image and radiologist mainly connect with using the centralized cloud PACS. There is always predefined advantage that can be achieved in migration of the concept of the cloud. The main advantage that can be stated is that the processing of the image is not at the browser side rather it is on the server side. This would directly involve less mistake and would directly involve efficiency in different sectors. References Wan, Yanli, et al. "Analysis and Design of Multi-pattern Retrieval Scheme of Regional PACS System Based Medical Images." Information Technology in Medicine and Education (ITME), 2016 8th International Conference on. IEEE, 2016. Bellam, Ravindra Babu, et al. "Issues while migrating medical imaging services on cloud based infrastructure." Next Generation Computing Technologies (NGCT), 2015 1st International Conference on. IEEE, 2015. Hai, Jinjin, et al. "Fast medical image segmentation based on patch sharing." Image, Vision and Computing (ICIVC), 2017 2nd International Conference on. IEEE, 2017. Lei, Wang, Wang Xi-lian, and Yuan Ke-hong. "Design and implementation of remote medical image reading and diagnosis system based on cloud services." Medical Imaging Physics and Engineering (ICMIPE), 2013 IEEE International Conference on. IEEE, 2013. Godinho, Tiago Marques, Lus Marques Silva, and Carlos Costa. "An automation framework for PACS workflows optimization in shared environments." Information Systems and Technologies (CISTI), 2015 10th Iberian Conference on. IEEE, 2015. da Silva Cordeiro, Saulo, et al. "A Risk Analysis Model for PACS Environments in the Cloud." 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