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2D and 3D Shape Based Segmentation Using Deformable Models. #S32. Ayman El-Baz 1 , Seniha E. Yuksel 1 , Hongjian Shi 1 , Aly A. Farag 1 , Mohamed A. El-Ghar 2 , Tarek Eldiasty 2 , Mohamed A. Ghoneim 2 1 Computer Vision and Image Processing Lab, University of Louisville - PowerPoint PPT Presentation
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Ayman El-Baz1, Seniha E. Yuksel1, Hongjian Shi1, Aly A. Farag1, Mohamed A. El-Ghar2, Tarek Eldiasty2, Mohamed A. Ghoneim2
1Computer Vision and Image Processing Lab, University of Louisville2Mansoura University, Urology and Nephrology Center
2D and 3D Shape Based Segmentation Using Deformable Models
#S32
Prior Shape
Current Gray Level
Deformable Model
1 1 1( ( )) ( | ) ( | )ext s gp d k p q k
2 2 2( ( )) ( | ) ( | )ext s gp d k p q k
2 1ext ext
Assuming object:
Assuming background:
Evolve if:Signed distance map and gray level densities are estimated with our modified EM algorithm
Results & Validation
Average shape by FEM
Result at-1.9dB SNR