A comparision of Model-based methods for knee cartilage segmentation

J. Cheong, N. Faggian, G. Langs, D. Suter, F. Cicuttini

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee cartilage from magnetic resonance imaging (MRI) scans facilitates the measurement of cartilage volume present in a patient's knee, thus enabling medical clinicians to detect the onset of osteoarthritis and also crucially, to study its effects. This paper compares four model-based segmentation methods popular for medical data segmentation, namely Active Shape Models (ASM) (Cootes et al., 1995), Active Appearance Models (AAM) (Cootes et al., 2001), Patch-based Active Appearance Models (PAAM) (Faggian et al., 2006), and Active Feature Models (AFM) (Langs et al., 2006). A comprehensive analysis of how accurately these methods segment human tibial cartilage is presented. The results obtained were benchmarked against the current "gold standard" (cartilage segmented manually by trained clinicians) and indicate that modeling local texture features around each landmark provides the best results for segmenting human tibial cartilage.

Original languageEnglish
Pages290-295
Number of pages6
Publication statusPublished - Jan 2007
EventInternational Conference on Vision Theory and Applications 2007 - Barcelona, Spain
Duration: 8 Mar 200711 Mar 2007
Conference number: 2nd
https://link.springer.com/book/10.1007%2F978-3-540-89682-1 (Proceedings)

Conference

ConferenceInternational Conference on Vision Theory and Applications 2007
Abbreviated titleVISAPP 2007
CountrySpain
CityBarcelona
Period8/03/0711/03/07
Internet address

Keywords

  • Cartilage
  • Model-Based
  • Osteoarthritis
  • Segmentation

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