Automatic fascia extraction and classification for measurement of muscle layer thickness

Tsubasa Imaizumi; Norihiro Koizumi; Ryosuke Kondo; Yu Nishiyama; Naoki Matsumoto

doi:10.1109/URAI.2018.8441877

Automatic fascia extraction and classification for measurement of muscle layer thickness

Tsubasa Imaizumi, Norihiro Koizumi, Ryosuke Kondo, Yu Nishiyama, Naoki Matsumoto

School of Medicine

The University of Electro-Communications

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this report, we proposed a method of discriminating of fascia using Histograms of Oriented Gradients (HOG) and Support Vector Machine (SVM) in ultrasound images. In modern society, aging is progressing due to medical development. Along with that, the decline of muscle due to aging is regarded as a serious problem. To cope with this problem, we proposed a method of automatic fascia classification to visualize muscle thickness. Our method use SVM based on the texture of ultrasound images. In addition to this method, our method achieves about 90% Accuracy and Recall by considering that the fascia is a continuous tissue. Experimental results show the effectiveness of our proposed automatic fascia extraction method.

Original language	English
Title of host publication	2018 15th International Conference on Ubiquitous Robots, UR 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	493-496
Number of pages	4
ISBN (Print)	9781538663349
DOIs	https://doi.org/10.1109/URAI.2018.8441877
Publication status	Published - 20 Aug 2018
Event	15th International Conference on Ubiquitous Robots, UR 2018 - Honolulu, United States Duration: 27 Jun 2018 → 30 Jun 2018

Publication series

Name	2018 15th International Conference on Ubiquitous Robots, UR 2018

Conference

Conference	15th International Conference on Ubiquitous Robots, UR 2018
Country/Territory	United States
City	Honolulu
Period	27/06/18 → 30/06/18

Keywords

machine learning
robot vision
ultrasound image

Access to Document

10.1109/URAI.2018.8441877

Cite this

Imaizumi, T., Koizumi, N., Kondo, R., Nishiyama, Y., & Matsumoto, N. (2018). Automatic fascia extraction and classification for measurement of muscle layer thickness. In 2018 15th International Conference on Ubiquitous Robots, UR 2018 (pp. 493-496). Article 8441877 (2018 15th International Conference on Ubiquitous Robots, UR 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/URAI.2018.8441877

@inproceedings{f9c93a2a1b89465caeba5d4c3359cfa7,

title = "Automatic fascia extraction and classification for measurement of muscle layer thickness",

abstract = "In this report, we proposed a method of discriminating of fascia using Histograms of Oriented Gradients (HOG) and Support Vector Machine (SVM) in ultrasound images. In modern society, aging is progressing due to medical development. Along with that, the decline of muscle due to aging is regarded as a serious problem. To cope with this problem, we proposed a method of automatic fascia classification to visualize muscle thickness. Our method use SVM based on the texture of ultrasound images. In addition to this method, our method achieves about 90% Accuracy and Recall by considering that the fascia is a continuous tissue. Experimental results show the effectiveness of our proposed automatic fascia extraction method.",

keywords = "machine learning, robot vision, ultrasound image",

author = "Tsubasa Imaizumi and Norihiro Koizumi and Ryosuke Kondo and Yu Nishiyama and Naoki Matsumoto",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th International Conference on Ubiquitous Robots, UR 2018 ; Conference date: 27-06-2018 Through 30-06-2018",

year = "2018",

month = aug,

day = "20",

doi = "10.1109/URAI.2018.8441877",

language = "English",

isbn = "9781538663349",

series = "2018 15th International Conference on Ubiquitous Robots, UR 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "493--496",

booktitle = "2018 15th International Conference on Ubiquitous Robots, UR 2018",

}

Imaizumi, T, Koizumi, N, Kondo, R, Nishiyama, Y & Matsumoto, N 2018, Automatic fascia extraction and classification for measurement of muscle layer thickness. in 2018 15th International Conference on Ubiquitous Robots, UR 2018., 8441877, 2018 15th International Conference on Ubiquitous Robots, UR 2018, Institute of Electrical and Electronics Engineers Inc., pp. 493-496, 15th International Conference on Ubiquitous Robots, UR 2018, Honolulu, United States, 27/06/18. https://doi.org/10.1109/URAI.2018.8441877

Automatic fascia extraction and classification for measurement of muscle layer thickness. / Imaizumi, Tsubasa; Koizumi, Norihiro; Kondo, Ryosuke et al.
2018 15th International Conference on Ubiquitous Robots, UR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 493-496 8441877 (2018 15th International Conference on Ubiquitous Robots, UR 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic fascia extraction and classification for measurement of muscle layer thickness

AU - Imaizumi, Tsubasa

AU - Koizumi, Norihiro

AU - Kondo, Ryosuke

AU - Nishiyama, Yu

AU - Matsumoto, Naoki

PY - 2018/8/20

Y1 - 2018/8/20

N2 - In this report, we proposed a method of discriminating of fascia using Histograms of Oriented Gradients (HOG) and Support Vector Machine (SVM) in ultrasound images. In modern society, aging is progressing due to medical development. Along with that, the decline of muscle due to aging is regarded as a serious problem. To cope with this problem, we proposed a method of automatic fascia classification to visualize muscle thickness. Our method use SVM based on the texture of ultrasound images. In addition to this method, our method achieves about 90% Accuracy and Recall by considering that the fascia is a continuous tissue. Experimental results show the effectiveness of our proposed automatic fascia extraction method.

AB - In this report, we proposed a method of discriminating of fascia using Histograms of Oriented Gradients (HOG) and Support Vector Machine (SVM) in ultrasound images. In modern society, aging is progressing due to medical development. Along with that, the decline of muscle due to aging is regarded as a serious problem. To cope with this problem, we proposed a method of automatic fascia classification to visualize muscle thickness. Our method use SVM based on the texture of ultrasound images. In addition to this method, our method achieves about 90% Accuracy and Recall by considering that the fascia is a continuous tissue. Experimental results show the effectiveness of our proposed automatic fascia extraction method.

KW - machine learning

KW - robot vision

KW - ultrasound image

UR - http://www.scopus.com/inward/record.url?scp=85053490503&partnerID=8YFLogxK

U2 - 10.1109/URAI.2018.8441877

DO - 10.1109/URAI.2018.8441877

M3 - Conference contribution

AN - SCOPUS:85053490503

SN - 9781538663349

T3 - 2018 15th International Conference on Ubiquitous Robots, UR 2018

SP - 493

EP - 496

BT - 2018 15th International Conference on Ubiquitous Robots, UR 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th International Conference on Ubiquitous Robots, UR 2018

Y2 - 27 June 2018 through 30 June 2018

ER -

Imaizumi T, Koizumi N, Kondo R, Nishiyama Y, Matsumoto N. Automatic fascia extraction and classification for measurement of muscle layer thickness. In 2018 15th International Conference on Ubiquitous Robots, UR 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 493-496. 8441877. (2018 15th International Conference on Ubiquitous Robots, UR 2018). doi: 10.1109/URAI.2018.8441877

Automatic fascia extraction and classification for measurement of muscle layer thickness

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this