[vc_empty_space][vc_empty_space]
Low-bitrate medical image compression
Setiawan A.D.a, Mengko T.L.R.a, Suksmono A.B.a, Gunawan H.a
a Institute Technology of 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]Medical imaging is an important element in the medical diagnostic process. However, storing medical images in a certain format is quite challenging. The size of a digital medical image is in the range of 3-32 MB per examination, depending on the imaging modality. Image compression will be a suitable solution when it comes to minimizing the size of stored medical images. The quest to find a compression method that has very good quality image reconstruction and a high compression ratio is limited by the Shanon/Nyquist sampling theorem and image entropy. However, in recent years, compressive sampling (CS) has arisen as an alternative sampling method. It guarantees the reconstruction of a high-quality signal from a small number of samples, and can be carried out if the signal is sparse enough. A set of trained bases grouped as a dictionary will guarantee the sparsest representation of the signal. This paper propose a low bit-rate medical image compression scheme based on CS and an overcomplete bases set to represent the medical images as sparse as possible.[/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]Bit rates,Compression methods,Compressive sampling,Digital medical images,High compression ratio,High quality,Image entropy,Imaging modality,Medical diagnostics,Medical image compression,Medical images,Number of samples,Over-complete,Quality image,Sampling method,Sampling theorems,Suitable solutions[/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][/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][/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]