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Computer-aided diagnosis method for detecting early esophageal cancer from endoscopic image by using dyadic wavelet transform and fractal dimension

発表形態:
原著論文
主要業績:
主要業績
単著・共著:
共著
発表年月:
2016年04月
DOI:
10.1007/978-3-319-32467-8_80
会議属性:
国際会議(国内開催を含む)
査読:
有り
リンク情報:

日本語フィールド

著者:
Ryuji Ohura, Hajime Omura. ,Yasuhisa Sakata, Teruya MInamoto
題名:
Computer-aided diagnosis method for detecting early esophageal cancer from endoscopic image by using dyadic wavelet transform and fractal dimension
発表情報:
Advances in Intelligent Systems and Computing 巻: 448 ページ: 929-938
キーワード:
概要:
抄録:
We propose a new computer-aided method for diagnosing early esophageal cancer from endoscopic images by using the dyadic wavelet transform (DYWT) and the fractal dimension. In our method, an input image is converted into HSV color space, and a fusion image is made from the S (saturation) and V (value) components based on the DYWT. We apply the contrast enhancement to produce a grayscale image in which the structure of abnormal regions is enhanced. We can obtain binary images composed of multiple layers by low-gradation processing. We visualize abnormal regions by summing these fractal dimensions by computing the complexity of these images. We describe a process for enhancing, detecting and visualizing abnormal regions in detail, and we present experimental results demonstrating that our method gives visualized images in which abnormal regions in endoscopic images can be located and that contain data useful for actual diagnosis of early esophageal cancer.

英語フィールド

Author:
Ryuji Ohura, Hajime Omura. ,Yasuhisa Sakata, Teruya MInamoto
Title:
Computer-aided diagnosis method for detecting early esophageal cancer from endoscopic image by using dyadic wavelet transform and fractal dimension
Announcement information:
Advances in Intelligent Systems and Computing Vol: 448 Page: 929-938
An abstract:
We propose a new computer-aided method for diagnosing early esophageal cancer from endoscopic images by using the dyadic wavelet transform (DYWT) and the fractal dimension. In our method, an input image is converted into HSV color space, and a fusion image is made from the S (saturation) and V (value) components based on the DYWT. We apply the contrast enhancement to produce a grayscale image in which the structure of abnormal regions is enhanced. We can obtain binary images composed of multiple layers by low-gradation processing. We visualize abnormal regions by summing these fractal dimensions by computing the complexity of these images. We describe a process for enhancing, detecting and visualizing abnormal regions in detail, and we present experimental results demonstrating that our method gives visualized images in which abnormal regions in endoscopic images can be located and that contain data useful for actual diagnosis of early esophageal cancer.


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