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A comparative study of colour retinal image coding using vector quantization: K-Means and fuzzy c-means
a Biomedical Engineering – School of Electrical Engineering and Informatics, 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]Retinal colour images play an important role in supporting medical diagnosis. Digital retinal image usually are represented in such a large data volume that takes a considerable amount of time to be accessed and displayed. Digital medical image coding therefore become crucial in medical image transfer and storage in electronic medical record server. This paper is concerned to compare the vector quantization (VQ) coding using K-Means and Fuzzy C-Means algorithms. This research investigates the performance of each algorithm: objective (PSNR value) and subjective (visual). The VQ coding scheme is conducted separately to image components in each RGB channel. Reconstructed colour image is obtained by combining the VQ decoding result of each image channel. The 444 combination (coding of the R, G and B channels by the size of 4×4) produces the best subjective and objective quality of image coding. However, the optimum colour models for teleophthalmology and electronic medical record is 848 combination due to the file size, objective and subjective quality. © 2009 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]Colour image,Colour models,Comparative studies,Digital medical images,Electronic medical record,File sizes,Fuzzy C mean,Fuzzy C-means algorithms,Image channels,Image components,K-means,Large data volumes,Medical diagnosis,Medical images,Retinal image,Subjective quality,Teleophthalmology,VQ 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]Colour retinal image,Fuzzy c-means,KMeans,Vector quantization[/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/ICEEI.2009.5254807[/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]