| Item type |
[ELS]学術雑誌論文 / Journal Article(1) |
| 公開日 |
2017-05-24 |
| タイトル |
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タイトル |
Computer-aided Diagnosis of Breast Elastography |
|
言語 |
en |
| 言語 |
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|
言語 |
eng |
| キーワード |
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言語 |
en |
|
主題 |
breast ultrasound |
| キーワード |
|
|
言語 |
en |
|
主題 |
elastography |
| キーワード |
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|
言語 |
en |
|
主題 |
computer-aided diagnosis |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 雑誌書誌ID |
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|
収録物識別子 |
AA00629581 |
| 論文名よみ |
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|
タイトル |
Computer-aided Diagnosis of Breast Elastography |
| 著者 |
Takada, Etsuo
Chang, Ruey-Feng
Shen, Wei-Chih
Ho, Yu-Chun
Woo, Kyung Moon
Nakajima, Michiko
Kobayashi, Masayuki
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| 著者所属(日) |
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| 著者所属(日) |
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| 著者所属(日) |
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| 著者所属(英) |
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en |
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Center of Medical Ultrasonics Dokkyo Medical University School of Medicine |
| 著者所属(英) |
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|
en |
|
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Department of Computer Science and Information Engineering National Taiwan University |
| 著者所属(英) |
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en |
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Department of Computer Science and Information Engineering National Chung Cheng University |
| 著者所属(英) |
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en |
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Department of Computer Science and Information Engineering National Taiwan University |
| 著者所属(英) |
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en |
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Department of Diagnostic Radiology, College of Medicine Seoul National University Hospital |
| 著者所属(英) |
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en |
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Comprehensive Regional Medical Service, Saitama Medical University |
| 著者所属(英) |
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en |
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Comprehensive Regional Medical Service, Saitama Medical University |
| 記事種別(日) |
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内容記述タイプ |
Other |
|
内容記述 |
原著 |
| 記事種別(英) |
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内容記述タイプ |
Other |
|
内容記述 |
Original |
| 抄録(英) |
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|
内容記述タイプ |
Other |
|
内容記述 |
Ultrasonography has been an important imaging technique for detecting breast tumors. As opposed tothe conventional B-mode image, the real-time tissue elastography by ultrasound is a new technique for imagingthe elasticity and applied to detect the stiffness of tissues. The red region of color elastography indicatesthe soft tissue and the blue one indicates the hard tissue. The harder tissue usually is classified as malignancy.In this paper, the authors proposed a computer-aided diagnosis( CAD) system on elastography tomeasure whether this system is effective and accurate to classify the tumor into benign and malignant. Accordingto the features of elasticity, the color elastography was transferred to hue, saturation, and value(HSV) color space and extracted meaningful features from hue images. Then the neural network was utilizedin multiple features to distinguish tumors. In this experiment, there are 180 pathology-proven cases including113 benign and 67 malignant cases used to examine the classification. The results of the proposedsystem showed an accuracy of 83.89 %, a sensitivity of 82.09 % and a specificity of 84.96 %. Compared withthe physician's diagnosis, an accuracy of 78.33 %, a sensitivity of 53.73 % and a specificity of 92.92 %, theproposed CAD system had better performance. Moreover, the agreement of the proposed CAD system andthe physician's diagnosis was calculated by kappa statistics, the kappa 0.64 indicated there is a fair agreementof observers. |
| 書誌情報 |
Dokkyo journal of medical sciences
巻 36,
号 1,
p. 17-25,
発行日 2009-03-25
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
03855023 |
KJ00006350139.pdf (646.9 kB)
