The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Propomos um novo método de compressão de imagens médicas sem perdas baseado na técnica de classificação hierárquica. A classificação hierárquica é uma técnica para obter alta taxa de compressão, detectando as regiões onde o padrão da imagem varia abruptamente e classificando a ordem dos pixels por seu valor para aumentar a previsibilidade. Neste método, podemos controlar a precisão da classificação junto com o tamanho e a complexidade. Como resultado, podemos reduzir os tamanhos das tabelas de permutação e reutilizá-las para outras regiões da imagem. A comparação usando a implementação experimental deste método mostra melhor desempenho para conjuntos de imagens médicas medidas por instrumentos de tomografia computadorizada e ressonância magnética de raios X, onde padrões de sub-blocos semelhantes aparecem com frequência. Esta técnica aplica o método de divisão quad-tree para dividir uma imagem em blocos, a fim de suportar decodificação progressiva e visualização rápida de imagens grandes.
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Atsushi MYOJOYAMA, Tsuyoshi YAMAMOTO, "A Lossless Image Compression for Medical Images Based on Hierarchical Sorting Technique" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 1, pp. 108-114, January 2002, doi: .
Abstract: We propose new lossless medical image compression method based on hierarchical sorting technique. Hierarchical sorting is a technique to achieve high compression ratio by detecting the regions where image pattern varies abruptly and sorting pixel order by its value to increase predictability. In this method, we can control sorting accuracy along with size and complexity. As the result, we can reduce the sizes of the permutation-tables and reuse the tables to other image regions. Comparison using experimental implementation of this method shows better performance for medical image set measured by X-ray CT and MRI instruments where similar sub-block patterns appear frequently. This technique applies quad-tree division method to divide an image to blocks in order to support progressive decoding and fast preview of large images.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_1_108/_p
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@ARTICLE{e85-d_1_108,
author={Atsushi MYOJOYAMA, Tsuyoshi YAMAMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={A Lossless Image Compression for Medical Images Based on Hierarchical Sorting Technique},
year={2002},
volume={E85-D},
number={1},
pages={108-114},
abstract={We propose new lossless medical image compression method based on hierarchical sorting technique. Hierarchical sorting is a technique to achieve high compression ratio by detecting the regions where image pattern varies abruptly and sorting pixel order by its value to increase predictability. In this method, we can control sorting accuracy along with size and complexity. As the result, we can reduce the sizes of the permutation-tables and reuse the tables to other image regions. Comparison using experimental implementation of this method shows better performance for medical image set measured by X-ray CT and MRI instruments where similar sub-block patterns appear frequently. This technique applies quad-tree division method to divide an image to blocks in order to support progressive decoding and fast preview of large images.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - A Lossless Image Compression for Medical Images Based on Hierarchical Sorting Technique
T2 - IEICE TRANSACTIONS on Information
SP - 108
EP - 114
AU - Atsushi MYOJOYAMA
AU - Tsuyoshi YAMAMOTO
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2002
AB - We propose new lossless medical image compression method based on hierarchical sorting technique. Hierarchical sorting is a technique to achieve high compression ratio by detecting the regions where image pattern varies abruptly and sorting pixel order by its value to increase predictability. In this method, we can control sorting accuracy along with size and complexity. As the result, we can reduce the sizes of the permutation-tables and reuse the tables to other image regions. Comparison using experimental implementation of this method shows better performance for medical image set measured by X-ray CT and MRI instruments where similar sub-block patterns appear frequently. This technique applies quad-tree division method to divide an image to blocks in order to support progressive decoding and fast preview of large images.
ER -