Open AccessPerformance of DICOM Data De-identification Process in a Single Board Computer
Author(s) -
Kadek Yota Ernanda Aryanto,
A.A. Gede Yudhi Paramartha
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1077/1/012069
Subject(s) - dicom , computer science , raspberry pi , identification (biology) , modality (human–computer interaction) , computer graphics (images) , process (computing) , image processing , computer vision , artificial intelligence , image (mathematics) , operating system , embedded system , botany , biology , internet of things
This work focused on testing the Raspberry Pi device’s performance with the MIRC CTP application, developed by RSNA. The application could de-identify medical image data. The medical images tested in this research were using the DICOM standard. The results have shown that the average data processing time for 100 DICOM files in CT modality was roughly 0.5 seconds per images. Meanwhile, the tests obtained an average of three images per seconds when more extensive data with average size of 132 KB were transferred, using 1000, 5000, and 10000 files, respectively. It was shown that the installation of the RSNA CTP in the Raspberry Pi device is not complicated. Furthermore, the transfer results demonstrated that the device could be used in the medical image data de-identification, specifically in image data sharing