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Distributed system for medical image transfer using wavelet-based dynamic RoI coding
Rahmiati P.a, Fajri A.a, Handayani A.a, Mengko T.L.R.a, Suksmono A.B.a, Pramudito J.T.a
a Electrical Engineering Department, Telecommunication Laboratory, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]A medical image usually has a large amount of data in order to get a high image quality which is crucial to support a right diagnosis. Therefore lossless compression schemes play a key role in saving and transferring a medical image data. Since there is a trade-off between reconstructed image quality and achieved compression ratio, an optimum compression ratio is important. In this paper, we propose a distributed system for medical image transfer using wavelet-based image coding with scalable reconstruction quality on dynamic user-defined region of interest (Rol). This proposed medical image transfer scheme adopts wavelet techniques and layered image representation structure of the JPEG2000 Part 1 image coding standard to provide quality refinement over specific area of the transferred image. We will compare the performance between distributed and non-distributed system. A laboratory experiment evaluating the required time to process a 1024×1024 pixels image shows that the distributed system require less time processing than the non-distributed system with better compression ratio. © 2005 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Distributed computing,JPEG2000,Resolution layer,Rol coding[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Distributed computing,Java,JPEG2000,Resolution layer,Rol coding,Telemedicine,Wavelet[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/HEALTH.2005.1500438[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]