TY - CHAP
T1 - Challenges in Processing Medical Images in Mobile Devices
AU - Curiel, Mariela
AU - Flórez-Valencia, Leonardo
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - To provide an adequate and timely response to patients, medical images for supporting diagnosis must be easily and efficiently accessed by health professionals anywhere at anytime. The widely adopted technology of mobile devices, especially smartphones, can provide this ubiquity. Additionally, due to the advances in processors with low power consumption, mobile devices such as tablets and smartphones can handle computationally intensive applications. Therefore, we can also take advantage of these devices to process images that require substantial amounts of resources in a distributed way. This article examines several technical issues involved in using smartphones for the distributed processing of medical images. Issues include the execution in the Android OS of already existing applications and their scheduling taking into account mobile devices’ characteristics. The exposition is done from the lessons learned using BOINC as an execution platform.
AB - To provide an adequate and timely response to patients, medical images for supporting diagnosis must be easily and efficiently accessed by health professionals anywhere at anytime. The widely adopted technology of mobile devices, especially smartphones, can provide this ubiquity. Additionally, due to the advances in processors with low power consumption, mobile devices such as tablets and smartphones can handle computationally intensive applications. Therefore, we can also take advantage of these devices to process images that require substantial amounts of resources in a distributed way. This article examines several technical issues involved in using smartphones for the distributed processing of medical images. Issues include the execution in the Android OS of already existing applications and their scheduling taking into account mobile devices’ characteristics. The exposition is done from the lessons learned using BOINC as an execution platform.
KW - Distributed processing
KW - Medical images
KW - Mobile grids
KW - Smartphones
UR - http://www.scopus.com/inward/record.url?scp=85119427147&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-75945-2_2
DO - 10.1007/978-3-030-75945-2_2
M3 - Chapter
AN - SCOPUS:85119427147
T3 - EAI/Springer Innovations in Communication and Computing
SP - 31
EP - 51
BT - EAI/Springer Innovations in Communication and Computing
PB - Springer Science and Business Media Deutschland GmbH
ER -