Coupled nonparametric shape priors for segmentation of multiple basal ganglia structures

Gokhan Uzunbas*, Mujdat Cetin, Gozde Unal, Aytul Ercil

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

6 Atıf (Scopus)

Özet

This paper presents a new method for multiple structure segmentation, using a maximum a posteriori (MAP) estimation framework, based on prior shape densities involving nonparametric multivariate kernel density estimation of multiple shapes. Our method is motivated by the observation that neighboring or coupling structures in medical images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our technique allows simultaneous segmentation of multiple objects, where highly contrasted, easy-to-segment structures can help improve the segmentation of weakly contrasted objects. We demonstrate the effectiveness of our method on both synthetic images and real magnetic resonance images (MRI) for segmentation of basal ganglia structures.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2008 5th IEEE International Symposium on Biomedical Imaging
Ana bilgisayar yayını alt yazısıFrom Nano to Macro, Proceedings, ISBI
Sayfalar217-220
Sayfa sayısı4
DOI'lar
Yayın durumuYayınlandı - 2008
Harici olarak yayınlandıEvet
Etkinlik2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
Süre: 14 May 200817 May 2008

Yayın serisi

Adı2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

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???event.eventtypes.event.conference???2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
Ülke/BölgeFrance
ŞehirParis
Periyot14/05/0817/05/08

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