Iteratively learning a liver segmentation using probabilistic atlases: preliminary results

Domingo J., Dura E., GÖÇERİ E.

15th IEEE International Conference on Machine Learning and Applications (ICMLA), California, Amerika Birleşik Devletleri, 18 - 20 Aralık 2016, ss.593-598 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/icmla.2016.194
  • Basıldığı Şehir: California
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.593-598
  • Anahtar Kelimeler: liver segmentation, probabilistic atlas, viscous reconstruction
  • Akdeniz Üniversitesi Adresli: Evet

Özet

This works deals with the concept of liver segmentation by using a priori information based on probabilistic atlases and segmentation learning based of previous steps. A probabilistic atlas is here understood as a probability or membership map that tells how likely is that a point belongs to a shape drawn from the shape distribution at hand. We devise a procedure to segment Perfusion Magnetic Resonance liver images that combines both: a probabilistic atlas of the liver and a segmentation algorithm based on global information of previous simpler segmentation steps, local information from close segmented slices and finally a mathematical morphology procedure, namely viscous reconstruction, to fill the shape. Preliminary results of the algorithm are provided.