İnsan yüz dokularının bulanıklaşma, gürültü ve kısmi hacim modelleri içeren Bayesçi 3B MR-BT görüntü birleşmesi yöntemi kullanılarak otomatik bölütlenmesi.

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Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Türkiye

Tezin Onay Tarihi: 2012

Tezin Dili: İngilizce

Öğrenci: Emre Şener

Eş Danışman: ÜNAL ERKAN MUMCUOĞLU, UTKU KANOĞLU

Özet:

Segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification. In this study, a new Bayesian method for segmentation of facial tissues is presented. Segmentation classes include muscle, bone, fat, air and skin. The method incorporates a model to account for image blurring during data acquisition, a prior helping to reduce noise as well as a partial volume model. Regularization based on isotropic and directional Markov Random Field priors are integrated to the algorithm and their effects on segmentation accuracy are investigated. The Bayesian model is solved iteratively yielding tissue class labels at every voxel of an image. Sub-methods as variations of the main method are generated by switching on/off a combination of the models. Testing of the sub-methods are performed on two patients using single modality three-dimensional (3D) images as well as registered multi-modal 3D images (Magnetic Resonance and Computerized Tomography). Numerical, visual and statistical analyses of the methods are conducted. Improved segmentation accuracy is obtained through the use of the proposed image models and multi-modal data. The methods are also compared with the Level Set method and an adaptive Bayesiansegmentation method proposed in a previous study.