Unsupervised Body Hair Detection by Positive-Unlabeled Learning in Photoacoustic Image

Ryo Kikkawa, Hiroki Kajita, Nobuaki Imanishi, Sadakazu Aiso, Ryoma Bise

研究成果: Conference contribution

抄録

Photoacoustic (PA) imaging is a new imaging technology that can non-invasively visualize blood vessels and body hair in 3D. It is useful in cosmetic surgery for detecting body hair and computing metrics such as the number and thicknesses of hairs. Previous supervised body hair detection methods often do not work if the imaging conditions change from training data. We propose an unsupervised hair detection method. Hair samples were automatically extracted from unlabeled samples using prior knowledge about spatial structure. If hair (positive) samples and unlabeled samples are obtained, Positive Unlabeled (PU) learning becomes possible. PU methods can learn a binary classifier from positive samples and unlabeled samples. The advantage of the proposed method is that it can estimate an appropriate decision boundary in accordance with the distribution of the test data. Experimental results using real PA data demonstrate that the proposed approach effectively detects body hairs.

本文言語English
ホスト出版物のタイトル43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3349-3352
ページ数4
ISBN(電子版)9781728111797
DOI
出版ステータスPublished - 2021
外部発表はい
イベント43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico
継続期間: 2021 11月 12021 11月 5

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(印刷版)1557-170X

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
国/地域Mexico
CityVirtual, Online
Period21/11/121/11/5

ASJC Scopus subject areas

  • 信号処理
  • 生体医工学
  • コンピュータ ビジョンおよびパターン認識
  • 健康情報学

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