GRAPH WALKS FOR CLASSIFICATION OF HISTOPATHOLOGICAL IMAGES

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Olgun G., SÖKMENSÜER C., GÜNDÜZ DEMİR Ç.

IEEE 10th International Symposium on Biomedical Imaging - From Nano to Macro (ISBI), San-Francisco, Kostarika, 7 - 11 Nisan 2013, ss.1126-1129, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/isbi.2013.6556677
  • Basıldığı Şehir: San-Francisco
  • Basıldığı Ülke: Kostarika
  • Sayfa Sayıları: ss.1126-1129
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Hacettepe Üniversitesi Adresli: Evet

Özet

This paper reports a new structural approach for automated classification of histopathological tissue images. It has two main contributions: First, unlike previous structural approaches that use a single graph for representing a tissue image, it proposes to obtain a set of subgraphs through graph walking and use these subgraphs in representing the image. Second, it proposes to characterize subgraphs by directly using distribution of their edges, instead of employing conventional global graph features, and use these characterizations in classification. Our experiments on colon tissue images reveal that the proposed structural approach is effective to obtain high accuracies in tissue image classification.