抄録
Towards the establishment of the preventive medical care for the cerebral aneurysm, this paper proposes an SVM based method for building a discrimination function that classifies the presence or absence of the cerebral aneurysm using the cerebral blood vessel's shape features obtained from medical images such as MR images. Using the discrimination function, this paper explores how much each feature affects the onset of the cerebral aneurysm. This paper deals with the internal carotid artery (ICA). The blood vessel (ICA)'s shape features are extracted from medical images of 18 persons without cerebral aneurysm and 13 patients with a cerebral aneurysm. From the medical image, the cross sections and centerline of the ICA are obtained. The cross sections are divided into nine sections along the centerline. Shape features such as the cross sectional area, its circularity, curvature, torsion, length of the centerline and branch angles are obtained in each section; as a total, 113 features including the mean and variance of some features in each section are used for building the SVM. As a result of conducting the experiments, the accuracy for discriminating the presence/absence of the aneurysm by the SVM is 90.3%. In the obtained discrimination function, the coefficient values of the function can be considered how much the features affect the onset of the aneurysm. The features that could significantly cause the onset of the cerebral aneurysm are clarified, and the reasons why these features are significant are discussed.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 2831-2836 |
| ページ数 | 6 |
| ジャーナル | IS and T International Symposium on Electronic Imaging Science and Technology |
| 巻 | Part F138660 |
| DOI | |
| 出版物ステータス | Published - 2018 1 1 |
| イベント | Intelligent Robotics and Industrial Applications using Computer Vision 2018, IRIACV 2018 - Burlingame, United States 継続期間: 2018 1 28 → 2018 2 1 |
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ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Science Applications
- Human-Computer Interaction
- Software
- Electrical and Electronic Engineering
- Atomic and Molecular Physics, and Optics
これを引用
Discriminating the presence of the cerebral aneurysm using shape features obtained from medical images of the cerebral vessel. / Kikuchi, Kosei; Yagi, Takanobu; Rong, Xu; Ohya, Jun.
：: IS and T International Symposium on Electronic Imaging Science and Technology, 巻 Part F138660, 01.01.2018, p. 2831-2836.研究成果: Conference article
}
TY - JOUR
T1 - Discriminating the presence of the cerebral aneurysm using shape features obtained from medical images of the cerebral vessel
AU - Kikuchi, Kosei
AU - Yagi, Takanobu
AU - Rong, Xu
AU - Ohya, Jun
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Towards the establishment of the preventive medical care for the cerebral aneurysm, this paper proposes an SVM based method for building a discrimination function that classifies the presence or absence of the cerebral aneurysm using the cerebral blood vessel's shape features obtained from medical images such as MR images. Using the discrimination function, this paper explores how much each feature affects the onset of the cerebral aneurysm. This paper deals with the internal carotid artery (ICA). The blood vessel (ICA)'s shape features are extracted from medical images of 18 persons without cerebral aneurysm and 13 patients with a cerebral aneurysm. From the medical image, the cross sections and centerline of the ICA are obtained. The cross sections are divided into nine sections along the centerline. Shape features such as the cross sectional area, its circularity, curvature, torsion, length of the centerline and branch angles are obtained in each section; as a total, 113 features including the mean and variance of some features in each section are used for building the SVM. As a result of conducting the experiments, the accuracy for discriminating the presence/absence of the aneurysm by the SVM is 90.3%. In the obtained discrimination function, the coefficient values of the function can be considered how much the features affect the onset of the aneurysm. The features that could significantly cause the onset of the cerebral aneurysm are clarified, and the reasons why these features are significant are discussed.
AB - Towards the establishment of the preventive medical care for the cerebral aneurysm, this paper proposes an SVM based method for building a discrimination function that classifies the presence or absence of the cerebral aneurysm using the cerebral blood vessel's shape features obtained from medical images such as MR images. Using the discrimination function, this paper explores how much each feature affects the onset of the cerebral aneurysm. This paper deals with the internal carotid artery (ICA). The blood vessel (ICA)'s shape features are extracted from medical images of 18 persons without cerebral aneurysm and 13 patients with a cerebral aneurysm. From the medical image, the cross sections and centerline of the ICA are obtained. The cross sections are divided into nine sections along the centerline. Shape features such as the cross sectional area, its circularity, curvature, torsion, length of the centerline and branch angles are obtained in each section; as a total, 113 features including the mean and variance of some features in each section are used for building the SVM. As a result of conducting the experiments, the accuracy for discriminating the presence/absence of the aneurysm by the SVM is 90.3%. In the obtained discrimination function, the coefficient values of the function can be considered how much the features affect the onset of the aneurysm. The features that could significantly cause the onset of the cerebral aneurysm are clarified, and the reasons why these features are significant are discussed.
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UR - http://www.scopus.com/inward/citedby.url?scp=85052850599&partnerID=8YFLogxK
U2 - 10.2352/ISSN.2470-1173.2018.09.IRIACV-238
DO - 10.2352/ISSN.2470-1173.2018.09.IRIACV-238
M3 - Conference article
AN - SCOPUS:85052850599
VL - Part F138660
SP - 2831
EP - 2836
JO - IS and T International Symposium on Electronic Imaging Science and Technology
JF - IS and T International Symposium on Electronic Imaging Science and Technology
SN - 2470-1173
ER -