| 電子情報通信学会 研究会発表申込システム 講演論文 詳細 |
技報閲覧サービス [ログイン] 技報アーカイブ |
| トップに戻る | 前のページに戻る | [Japanese] / [English] |
| 講演抄録／キーワード | ||
| 講演名 | 2009-01-21 10:45 Fractal analysis and texture features of vascular networks observed in colonoscopy images as novel image classifiers for clinical metabolic syndrome Tsung-Chun Lee（National Taiwan Univ.）・○Syu-Jyun Peng・Hsuan-Ting Chang（National Yunlin Univ. of Science & Tech）・Jou-Wei Lin・Juey-Jen Hwang・Chien-Chiang Liu（National Taiwan Univ.）  MI2008-161 |
|
| 抄録 | （和） | In the study, we aim to analyze the texture characteristics of colonic vascular networks observed on colonoscopy, in order to test and set up biomedical imaging markers for clinical metabolic syndrome. We retrospectively selected clear colonoscopic images from 30 clinical metabolic syndrome subjects and 30 healthy control subjects. Regions of interest containing vascular networks of major trunk and primary branches were adopted. White light reflection points were detected and eliminated. With the wavelet transform, the fractal dimensions in different subbands are determined. In addition, six more statistical features are combined with the fractal dimension in the support vector machine (SVM) to differentiate two different groups of subjects. Experimental results show that 93.94%-96.97% classification accuracy can be achieved when using the combination of the fractal dimension and other different statistical feature. |
| （英） | In the study, we aim to analyze the texture characteristics of colonic vascular networks observed on colonoscopy, in order to test and set up biomedical imaging markers for clinical metabolic syndrome. We retrospectively selected clear colonoscopic images from 30 clinical metabolic syndrome subjects and 30 healthy control subjects. Regions of interest containing vascular networks of major trunk and primary branches were adopted. White light reflection points were detected and eliminated. With the wavelet transform, the fractal dimensions in different subbands are determined. In addition, six more statistical features are combined with the fractal dimension in the support vector machine (SVM) to differentiate two different groups of subjects. Experimental results show that 93.94%-96.97% classification accuracy can be achieved when using the combination of the fractal dimension and other different statistical feature. | |
| キーワード | （和） | fractal analysis / sedation colonoscopy / vascular network / metabolic syndrome / support vector machine / / / |
| （英） | fractal analysis / sedation colonoscopy / vascular network / metabolic syndrome / support vector machine / / / | |
| 文献情報 | 信学技報, vol. 108, no. 385, MI2008-161, pp. 455-460, 2009年1月. | |
| 資料番号 | MI2008-161 | |
| 発行日 | 2009-01-12 (MI) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | MI2008-161 |
|
[研究会発表申込システムのトップページに戻る]