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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo propõe um método de segmentação baseado em limiares auxiliado pela Álgebra de Kleene. Para uma determinada imagem incluindo algumas regiões de interesse (abreviadamente ROIs) com o nível de intensidade coerente, suponha que podemos segmentar cada ROI na aplicação da técnica de limiar. Três estados segmentados são então derivados para cada ROI: Escassez denotada pelo valor lógico 0, Correto denotado por 1 e Excesso denotado por 2. Os estados segmentados para cada ROI na imagem podem ser então expressos em um sistema lógico ternário. Nosso objetivo é então definido para encontrar o estado "Correto (1)" para cada ROI. Primeiro, é proposta a função unate, que é um modelo de procedimento baseado na Álgebra de Kleene. Porém, este método não é completo para alguns casos, ou seja, a proporção de segmentação correta é de cerca de 70% para segmentação de três e quatro ROI. Para os casos com falha, as operações de Brzozowski, que são definidas na álgebra de De Morgan, podem acomodar a localização completa de todos os estados "Corretos". Finalmente, aplicamos esses procedimentos a problemas de segmentação de uma imagem de RM do cérebro humano e de uma imagem de TC do pé. Como resultado, podemos encontrar todos os estados “1” para os ROIs, ou seja, podemos segmentar corretamente os ROIs.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copiar
Makoto ISHIKAWA, Naotake KAMIURA, Yutaka HATA, "Thresholding Based Image Segmentation Aided by Kleene Algebra" in IEICE TRANSACTIONS on Information,
vol. E82-D, no. 5, pp. 962-967, May 1999, doi: .
Abstract: This paper proposes a thresholding based segmentation method aided by Kleene Algebra. For a given image including some regions of interest (ROIs for short) with the coherent intensity level, assume that we can segment each ROI on applying thresholding technique. Three segmented states are then derived for every ROI: Shortage denoted by logic value 0, Correct denoted by 1 and Excess denoted by 2. The segmented states for every ROI in the image can be then expressed on a ternary logic system. Our goal is then set to find "Correct (1)" state for every ROI. First, unate function, which is a model of Kleene Algebra, based procedure is proposed. However, this method is not complete for some cases, that is, correctly segmented ratio is about 70% for three and four ROI segmentation. For the failed cases, Brzozowski operations, which are defined on De Morgan algebra, can accommodate to completely find all "Correct" states. Finally, we apply these procedures to segmentation problems of a human brain MR image and a foot CT image. As the result, we can find all "1" states for the ROIs, i. e. , we can correctly segment the ROIs.
URL: https://global.ieice.org/en_transactions/information/10.1587/e82-d_5_962/_p
Copiar
@ARTICLE{e82-d_5_962,
author={Makoto ISHIKAWA, Naotake KAMIURA, Yutaka HATA, },
journal={IEICE TRANSACTIONS on Information},
title={Thresholding Based Image Segmentation Aided by Kleene Algebra},
year={1999},
volume={E82-D},
number={5},
pages={962-967},
abstract={This paper proposes a thresholding based segmentation method aided by Kleene Algebra. For a given image including some regions of interest (ROIs for short) with the coherent intensity level, assume that we can segment each ROI on applying thresholding technique. Three segmented states are then derived for every ROI: Shortage denoted by logic value 0, Correct denoted by 1 and Excess denoted by 2. The segmented states for every ROI in the image can be then expressed on a ternary logic system. Our goal is then set to find "Correct (1)" state for every ROI. First, unate function, which is a model of Kleene Algebra, based procedure is proposed. However, this method is not complete for some cases, that is, correctly segmented ratio is about 70% for three and four ROI segmentation. For the failed cases, Brzozowski operations, which are defined on De Morgan algebra, can accommodate to completely find all "Correct" states. Finally, we apply these procedures to segmentation problems of a human brain MR image and a foot CT image. As the result, we can find all "1" states for the ROIs, i. e. , we can correctly segment the ROIs.},
keywords={},
doi={},
ISSN={},
month={May},}
Copiar
TY - JOUR
TI - Thresholding Based Image Segmentation Aided by Kleene Algebra
T2 - IEICE TRANSACTIONS on Information
SP - 962
EP - 967
AU - Makoto ISHIKAWA
AU - Naotake KAMIURA
AU - Yutaka HATA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E82-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 1999
AB - This paper proposes a thresholding based segmentation method aided by Kleene Algebra. For a given image including some regions of interest (ROIs for short) with the coherent intensity level, assume that we can segment each ROI on applying thresholding technique. Three segmented states are then derived for every ROI: Shortage denoted by logic value 0, Correct denoted by 1 and Excess denoted by 2. The segmented states for every ROI in the image can be then expressed on a ternary logic system. Our goal is then set to find "Correct (1)" state for every ROI. First, unate function, which is a model of Kleene Algebra, based procedure is proposed. However, this method is not complete for some cases, that is, correctly segmented ratio is about 70% for three and four ROI segmentation. For the failed cases, Brzozowski operations, which are defined on De Morgan algebra, can accommodate to completely find all "Correct" states. Finally, we apply these procedures to segmentation problems of a human brain MR image and a foot CT image. As the result, we can find all "1" states for the ROIs, i. e. , we can correctly segment the ROIs.
ER -