Implementation of Video and Medical Image Services in Cloud

Yang, Chao-Tung; Huang, Kuan-Lung; Chu, William Cheng‐Chung; Lai, Kuan‐Chou; Chang, Chih‐Hung; Lu, Chih-Wei

doi:10.1109/compsacw.2013.70

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Yang의 연구에서는 클라우드 기반 환자의 비디오 및 이미지 분석 서비스를 개발하였다 [1]. 이 서비스는 대용 량의 의료 이미지를 처리하기 위하여 Hadoop을 사용한 이미지 전송, 저장 등 작업을 수행한다.…”

Section: 관련 연구unclassified

A Cloud Service for Archiving and Interpreting Medical Images

Kim¹,

Park²,

Jung³

et al. 2016

Journal of Internet Computing and Services

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Medical images are an effective means to identity medical abnormalities.. Patients typically have medical images taken at different clinics during lifetime, and they often wish to have second interpretation on medical images showing substantial diseases. At present, since personal medical images are distributed to multiple clinics, there is a bit discomfort that patients directly bring their images by hands to get the second interpretation from another physician. With these two motivations, we design a cloud service for archiving medical images and interpreting medical images by physicians. We present the design and implementation of the service, and show its practical value as low-cost personal healthcare service. By using the service, patients can retrieve and review their medical images anytime and have a convenience of acquiring second opinions on their medical images at low-cost without visiting a clinic.

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Section: 관련 연구unclassified

A Cloud Service for Archiving and Interpreting Medical Images

Kim¹,

Park²,

Jung³

et al. 2016

Journal of Internet Computing and Services

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Parallel Image Texture Feature Extraction under Hadoop Cloud Platform

Zhu

Shen

Shang

et al. 2014

Intelligent Computing Theory

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Abstract.With the increasing amount of digital image data, massive image process and feature extraction process have become a time-consuming process. As an excellent mass data processing and storage capacity of the open source cloud platform, Hadoop provides a parallel computing model MapReduce, HDFS distributed file system module. Firstly, we introduced Hadoop platform programming framework and Tamura texture features. And then, the image processing and feature texture feature extraction calculations involved in the process to achieve Hadoop platform. The results which comparison with Matlab platform shows it is less obvious advantage of Hadoop platform in image processing and feature extraction of lower-resolution images, but for image processing and feature extraction of high-resolution images, the time spent in Hadoop platform is greatly reducing, data processing capability the advantages is obvious.

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