Automatic segmentation of human facial tissue by MRI-CT fusion: A feasibility study

Kale E. H., Mumcuoglu E. U., HAMCAN S.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, cilt.108, sa.3, ss.1106-1120, 2012 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 108 Sayı: 3
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.cmpb.2012.07.006
  • Dergi Adı: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1106-1120
  • Anahtar Kelimeler: Medical image segmentation, Human facial tissue, Data fusion, Partial volume, Level Sets, Bayesian, MUTUAL INFORMATION, ACTIVE CONTOURS, 3D SEGMENTATION, CLASSIFICATION, MUSCLES, SURGERY, IMAGES, MODEL, REGISTRATION, MAXIMIZATION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

The aim of this study was to develop automatic image segmentation methods to segment human facial tissue which contains very thin anatomic structures. The segmentation output can be used to construct a more realistic human face model for a variety of purposes like surgery planning, patient specific prosthesis design and facial expression simulation. Segmentation methods developed were based on Bayesian and Level Set frameworks, which were applied on three image types: magnetic resonance imaging (MRI), computerized tomography (CT) and fusion, in which case information from both modalities were utilized maximally for every tissue type. The results on human data indicated that fusion, thickness adaptive and postprocessing options provided the best muscle/fat segmentation scores in both Level Set and Bayesian methods. When the best Level Set and Bayesian methods were compared, scores of the latter were better. Number of algorithm parameters (to be trained) and computer run time measured were also in favour of the Bayesian method. (C) 2012 Elsevier Ireland Ltd. All rights reserved.