Download presentation
Presentation is loading. Please wait.
1
Tooth Segmentation on Dental Meshes Using Morphologic Skeleton
Computers & Graphics CAD/Graphics 2013, Hong Kong Tooth Segmentation on Dental Meshes Using Morphologic Skeleton Kan WU M.Eng. Li CHEN Ph.D. School of Software Tsinghua University, P. R. of China Jing LI Ph.D. Yanheng ZHOU Ph.D. Department of Orthodontics Peking University School and Hospital of Stomatology, P. R. of China
2
Background our work
3
Contribution an applicable pipeline for dental mesh segmentation
avoid complex mesh feature estimation significantly reduced user interaction experiments on various clinical cases of different tooth shapes and various levels of crowding problems
4
Problems of Current Work
not sufficiently accurate Kumar et al. 2011 affected by feature disturbance Kronfeld et al. 2010 intensive interaction “3Shape”
5
A Good Dental Segmentation Approach Should
locate teeth area automatically separate adjacent teeth automatically morphologic skeleton less dependent on complex feature estimation smoothed & fitted boundary
6
ACCURACY EFFICIENCY REDUCED INTERACTION Why Morphologic Skeleton
insensitive to feature missing & disturbance ACCURACY simplified approximation of mesh features EFFICIENCY easy separation of adjacent objects REDUCED INTERACTION
7
Dental Mesh Segmentation Pipeline
8
1st Step: Locating Teeth Parts
automatic plane cutting original mesh region-growing skeletonization
9
1st Step: Locating Teeth Parts – (1)Estimating Cutting Plane
PCA-based plane initialization energy field
10
(1)Estimating Cutting Plane – PCA-based Plane Initialization
barycentric point eigenvector corresponding to the smallest eigenvalue set of feature vertices Kronfeld et al., 2010
11
(1)Estimating Cutting Plane – Energy Field
weighted distance feature points connected to v
12
curvature threshholding connectivity filtering
1st Step: Locating Teeth Parts – (2)Morphologic Skeletonization skeleton curvature threshholding connectivity filtering morphologic operation skeletonization
13
1st Step: Locating Teeth Parts – (2)Morphologic Skeletonization
original morphologic skeleton (Rossl et al., 2000) improved morphologic skeleton
14
1st Step: Locating Teeth Parts – (3)Region-Growing
seed points skeleton
15
2nd Step: Separating Teeth
cut
16
2nd Step: Separating Teeth – Various Scenarios
discarded cut valid cut
17
2nd Step: Separating Teeth – Results
18
3rd Step: Smoothing Tooth Contours
3D contours interpolated 3D contours 2D contours sampled 2D contours sampled 3D contours
19
center point for contour
3rd Step: Smoothing Tooth Contours – 2D Sampling Direction Change Measure Length Change Measure center point for contour middle point
20
3rd Step: Smoothing Tooth Contours – 2D Sampling
Direction Change Measures Length Measures sign(x) = 1 if x > 0, otherwise -1
21
Results – Mild Tooth Crowding
original model cutting plane estimation skeletonization & region-growing separating & contour smoothing
22
Results – Moderate Tooth Crowding
original model cutting plane estimation skeletonization & region-growing separating & contour smoothing
23
Results – Severe Tooth Crowding
original model cutting plane estimation skeletonization & region-growing separating & contour smoothing
24
Results
25
Results
26
Results
27
Comparative Results – Published Approaches
Kronfeld et al. 2010 our approach Kumar et al. 2011 our approach
28
when user interaction is not sufficiently accurate enough
Comparative Results – “3Shape” Software “3Shape” Software our approach “3Shape” Software our approach when user interaction is not sufficiently accurate enough
29
Accuracy Evaluation – Mean Errors
The mean errors that compare our results to manually labeled ground truth. The unit is mm.
30
Accuracy Evaluation – Error Distribution
the distribution of particular error values across all segmented boundary vertices. The blue, yellow, red lines indicate the ranges of [0, 0.25], [0.25, 0.5], [0.5, 1.5], respectively.
31
User Interaction Evaluation
Time consumed by user interactions. The blue and yellow lines indicate manual boundary completion and additional seed adding, respectively. The unit is s
32
Limitations user interaction still needed no GPU accelerating Future Work a dental mesh benchmark GPU accelerating completely eliminate user interaction
33
Demo
34
Kan WU (ulmonkey1987@gmail.com)
THANK YOU Kan WU Li CHEN Jing LI Yanheng ZHOU
Similar presentations
