Lesion Type Classification by Applying Machine-Learning Technique to Contrast-Enhanced Ultrasound Images-Reference-Cited by-同舟云学术

Lesion Type Classification by Applying Machine-Learning Technique to Contrast-Enhanced Ultrasound Images

Published:2014 Issue:11 Volume:E97.D Page:2947-2954
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

TAKAGI Kazuya¹,KONDO Satoshi¹,NAKAMURA Kensuke²,TAKIGUCHI Mitsuyoshi²

Affiliation:

1. Panasonic Healthcare Co., Ltd.

2. Veterinary Teaching Hospital, Department of Veterinary Clinical Sciences, Graduate School of Veterinary Medicine, Hokkaido University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E97.D/11/E97.D_2013EDP7464/_pdf

Reference20 articles.

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2. [2] M. Kudo, K. Hatanaka, and K. Maekawa, “Sonazoid-enhanced Ultrasound in the Diagnosis and Treatment of Hepatic Tumors,” J. Med. Ultrasound, vol.16, no.2, pp.130-139, 2008.

3. [3] K. Numata, et. al., “Contrast enhanced ultrasound of hepatocellular carcinoma,” World J Radiol., vol.28, no.2, pp.68-82, Feb. 2010.

4. [4] H. Kanemoto, et al., “Vascular and Kupffer imaging of canine liver and spleen using the new contrast agent Sonazoid,” J. Vet. Med. Sci., vol.70, no.11, pp.1265-1268, Nov. 2008.

5. [5] K. Sugimoto, F. Moriyasu, N. Kamiyama, and K. Doi, “Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography,” Med Phys. vol.35, no.5, pp.1734-1746, May 2008.

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