Partial Least Squares回帰及び複数特徴量間HLAC特徴を用いた細胞内画像中の粒子計数

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2015/02/01

タイトル(英語): Particles Counting in Intracellular Images by Partial Least Squares Regression and HLAC Feature between Multiple Features

著者名: 熊谷　章平（名城大学 大学院 理工学研究科），堀田　一弘（名城大学 大学院 理工学研究科）

著者名(英語): Shohei Kumagai (Graduate School of Science and Technology, Meijo University), Kazuhiro Hotta (Graduate School of Science and Technology, Meijo University)

キーワード: 対象計数，細胞内画像処理，Partial Least Squares回帰，高次局所自己相関　　Target Counting，Intracellular Images，Partial Least Squares Regression，Higher-order Local Auto Correlation

要約(英語): In the field of cell biology, particle counting in intracellular images is important for investigating the cause of diseases. However, particles are manually counted by human observers now. Such manually counting takes a lot of time, and counting result becomes subjective. If an automatic counting method by computer is realized, we can treat a large number of images and it can obtain many objective data. The development of an automatic counting method much contributes to understand the case of disease. However, particle counting in intracellular images by a computer is new research field, and conventional methods are little. Thus, we propose counting method based on regression analysis. We use partial least squares regression and auto-correlation between 2 different types of features using mask patterns for higher-order local autocorrelation feature. The proposed method gives higher accuracy than counting by principal component regression, support vector regression and ImageJ. In experiment, the proposed method can count with small error in comparison with human counting. The effectiveness of our method is shown by experiments.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.2 （2015） 特集：フレキシブルフィルムデバイスのための材料・プロセスと評価技術

本誌掲載ページ: 236-243 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/2/135_236/_article/-char/ja/