REGION DETERMINATION METHOD

PROBLEM TO BE SOLVED: To provide a technique allowing for photographing a biological sample such as a cell in a bright field and automatically determining plural types of regions included in an obtained observation object image.SOLUTION: Firstly, a method includes photographing a known biological sa...

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Bibliographic Details
Main Authors MORIWAKI SANZO, HIKITA YUICHIRO, OGI HIROSHI, ITO KYOKO, KOKUBO MASAHIKO
Format Patent
LanguageEnglish
Japanese
Published 18.10.2018
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Summary:PROBLEM TO BE SOLVED: To provide a technique allowing for photographing a biological sample such as a cell in a bright field and automatically determining plural types of regions included in an obtained observation object image.SOLUTION: Firstly, a method includes photographing a known biological sample at a plurality of wavelength bands with different center wavelengths and thereby obtaining a plurality of learning images. Next, the method includes obtaining a wavelength profile of each pixel from the obtained learning images. Next, it includes classifying each pixel of the learning images into a plurality of clusters according to its wavelength profile. Then, it includes learning information on the clusters for each region included in the learning images. On the other hand, it includes obtaining images of an observation object biological sample in the same fashion, and classifying each pixel of the obtained images into a plurality of clusters. Then, it includes determining plural types of regions included in the observation object images on the basis of the information on the cluster and results of the learning. This allows the plural types of regions included in the observation object images to be automatically determined.SELECTED DRAWING: Figure 11 【課題】細胞等の生体試料を明視野で撮影するとともに、得られた観察対象画像に含まれる複数種類の領域を、自動的に判定できる技術を提供する。【解決手段】まず、既知の生体試料を中心波長の異なる複数の波長帯域で撮影することにより、複数の学習用画像を取得する。次に、取得した学習用画像について、画素ごとに、波長プロファイルを得る。続いて、波長プロファイルに応じて、学習用画像の各画素を複数のクラスターに分類する。その後、学習用画像に含まれる領域ごとに、クラスターの情報を学習する。一方、観察対象の生体試料についても、同様に画像を取得し、得られた画像の各画素を、複数のクラスターに分類する。そして、当該クラスターの情報と、上述した学習の結果とに基づいて、観察対象画像に含まれる複数種類の領域を判定する。これにより、観察対象画像に含まれる複数種類の領域を自動的に判定できる。【選択図】図11
Bibliography:Application Number: JP20170059952