JP6692049B2 - 識別装置および識別方法 - Google Patents
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Description
Claims (8)
- 細胞の光学的厚み分布の画像の特徴量を抽出する特徴量抽出部と、
種別が既知である既知細胞の光学的厚み分布の画像について前記特徴量抽出部により抽出された特徴量に基づいて機械学習を行った学習結果を記憶する記憶部と、
前記記憶部により記憶されている学習結果を用いて、種別が未知である未知細胞の光学的厚み分布の画像について前記特徴量抽出部により抽出された特徴量に基づいて未知細胞の種別を判定する識別部と、
を備え、
未知細胞の光学的厚み分布の画像の特徴量を抽出する際、または、未知細胞の種別を判定する際に、前記記憶部により記憶されている学習結果を用いるとともに、
前記特徴量抽出部が、光学的厚み分布の画像内の位置における光学的厚みの空間的変化量に関する情報を該画像の特徴量として抽出し、前記光学的厚みの空間的変化量に関する情報は、前記光学的厚み分布の画像内の位置における光学的厚みの傾き情報を表すベクトルの勾配強度および勾配方向の双方または何れか一方である、
識別装置。 - 既知細胞の光学的厚み分布の画像について前記特徴量抽出部により抽出された特徴量に基づいて機械学習を行う学習部を更に備え、
前記記憶部が、前記学習部による機械学習の学習結果を記憶する、
請求項1に記載の識別装置。 - 前記特徴量抽出部は、前記記憶部により記憶されている学習結果を用いて、前記未知細胞の光学的厚み分布の画像内において前記特徴量を抽出する領域を少なくとも1つ設定する、請求項1または2に記載の識別装置。
- 種別が未知である未知細胞の光学的厚み分布の画像の特徴量を特徴量抽出部により抽出する第1特徴量抽出ステップと、
種別が既知である既知細胞の光学的厚み分布の画像について前記特徴量抽出部により抽出された特徴量に基づいて機械学習を行って記憶部により記憶された当該学習結果を用いて、前記第1特徴量抽出ステップにおいて抽出された特徴量に基づいて未知細胞の種別を判定する識別ステップと、
を備え、
未知細胞の光学的厚み分布の画像の特徴量を抽出する際、または、未知細胞の種別を判定する際に、前記記憶部により記憶されている学習結果を用いるとともに、
前記特徴量抽出部により、光学的厚み分布の画像内の位置における光学的厚みの空間的変化量に関する情報を該画像の特徴量として抽出し、前記光学的厚みの空間的変化量に関する情報は、前記光学的厚み分布の画像内の位置における光学的厚みの傾き情報を表すベクトルの勾配強度および勾配方向の双方または何れか一方である、
識別方法。 - 既知細胞の光学的厚み分布の画像の特徴量を前記特徴量抽出部により抽出する第2特徴量抽出ステップと、
前記第2特徴量抽出ステップにおいて抽出された特徴量に基づいて機械学習を行い、その学習結果を前記記憶部に記憶させる学習ステップと、
を更に備える、請求項4に記載の識別方法。 - 前記第1特徴量抽出ステップは、前記記憶部により記憶された学習結果を用いて、前記未知細胞の光学的厚み分布の画像内において前記特徴量を抽出する領域を少なくとも1つを設定する、請求項4または5に記載の識別方法。
- 前記細胞として白血球およびがん細胞を含む、請求項4〜6の何れか一項に記載の識別方法。
- 前記特徴量抽出部により、溶血剤を添加した細胞の光学的厚み分布の画像の特徴量を抽出する、請求項7に記載の識別方法。
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