Automated Classification of Local Patches in Colon Histopathology


Kalkan H., Nap M., Duin R. P. W. , Loog M.

21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japonya, 11 - 15 Kasım 2012, ss.61-64 identifier identifier

  • Cilt numarası:
  • Basıldığı Şehir: Tsukuba
  • Basıldığı Ülke: Japonya
  • Sayfa Sayıları: ss.61-64

Özet

An automated histology analysis is proposed for classification of local image patches of colon histopathology images into four principle classes: normal, cancer, adenomatous and inflamed classes. Shape features based on stroma, lumen and imperfectly segmented nuclei are combined with texture features for classification. The classification is analyzed under the three scenarios: normal vs. abnormal, cancer vs. non-cancer and four-class classification on a labeled dataset consisting of 2000 patches per class which were collected from 55 different slices. The proposed method achieves 79.28% mean accuracy between normal and abnormal; 87.67% accuracy between cancer and non-cancer and 75.15% between the four classes with equal class priories.