A New Sulcus Extraction Algorithm Using MAGNET Principle

Shoji HIRANO; Naotake KAMIURA; Yutaka HATA

A New Sulcus Extraction Algorithm Using MAGNET Principle

Shoji HIRANO, Naotake KAMIURA, Yutaka HATA

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This paper presents a feature extraction model MAGNET' to find the deepest point of branched sulcus. Our model demonstrates magnetic principle and consists of four types of ideal magnetic poles: an N-pole and three S-poles. According to attractive or repulsive Coulomb forces between their poles, one of the S-poles is pushed to the deepest point of the sulcus. First, we explain our model on the simple sulcus model. Second, we apply it to the sulcus with implicit branches. Our model can detect the target points in every branch. Then an example to realize the model on a synthetic image is introduced. We apply our model to human brain MR images and human foot CT images. Experimental results on human brain MR images show that our method enable us to successfully detect the points.

Publication: IEICE TRANSACTIONS on Information Vol.E81-D No.11 pp.1253-1260

Publication Date: 1998/11/25

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Category: Image Processing,Computer Graphics and Pattern Recognition

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Shoji HIRANO, Naotake KAMIURA, Yutaka HATA, "A New Sulcus Extraction Algorithm Using MAGNET Principle" in IEICE TRANSACTIONS on Information, vol. E81-D, no. 11, pp. 1253-1260, November 1998, doi: .
Abstract: This paper presents a feature extraction model MAGNET' to find the deepest point of branched sulcus. Our model demonstrates magnetic principle and consists of four types of ideal magnetic poles: an N-pole and three S-poles. According to attractive or repulsive Coulomb forces between their poles, one of the S-poles is pushed to the deepest point of the sulcus. First, we explain our model on the simple sulcus model. Second, we apply it to the sulcus with implicit branches. Our model can detect the target points in every branch. Then an example to realize the model on a synthetic image is introduced. We apply our model to human brain MR images and human foot CT images. Experimental results on human brain MR images show that our method enable us to successfully detect the points.
URL: https://global.ieice.org/en_transactions/information/10.1587/e81-d_11_1253/_p

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@ARTICLE{e81-d_11_1253,
author={Shoji HIRANO, Naotake KAMIURA, Yutaka HATA, },
journal={IEICE TRANSACTIONS on Information},
title={A New Sulcus Extraction Algorithm Using MAGNET Principle},
year={1998},
volume={E81-D},
number={11},
pages={1253-1260},
abstract={This paper presents a feature extraction model MAGNET' to find the deepest point of branched sulcus. Our model demonstrates magnetic principle and consists of four types of ideal magnetic poles: an N-pole and three S-poles. According to attractive or repulsive Coulomb forces between their poles, one of the S-poles is pushed to the deepest point of the sulcus. First, we explain our model on the simple sulcus model. Second, we apply it to the sulcus with implicit branches. Our model can detect the target points in every branch. Then an example to realize the model on a synthetic image is introduced. We apply our model to human brain MR images and human foot CT images. Experimental results on human brain MR images show that our method enable us to successfully detect the points.},
keywords={},
doi={},
ISSN={},
month={November},}

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TY - JOUR
TI - A New Sulcus Extraction Algorithm Using MAGNET Principle
T2 - IEICE TRANSACTIONS on Information
SP - 1253
EP - 1260
AU - Shoji HIRANO
AU - Naotake KAMIURA
AU - Yutaka HATA
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E81-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 1998
AB - This paper presents a feature extraction model MAGNET' to find the deepest point of branched sulcus. Our model demonstrates magnetic principle and consists of four types of ideal magnetic poles: an N-pole and three S-poles. According to attractive or repulsive Coulomb forces between their poles, one of the S-poles is pushed to the deepest point of the sulcus. First, we explain our model on the simple sulcus model. Second, we apply it to the sulcus with implicit branches. Our model can detect the target points in every branch. Then an example to realize the model on a synthetic image is introduced. We apply our model to human brain MR images and human foot CT images. Experimental results on human brain MR images show that our method enable us to successfully detect the points.
ER -