Mandibular Canal Segmentation Based on Shape-Driven Level-set Algorithm Restrained by Local Information

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1 Chinese Journal of Biomedical Engineering Vol. 3 No. 2 April * CT PCA 4. 9%. 82% CT TP39. 4 A Mandibular Canal Segmentation Based on Shape-Driven Level-set Algorithm Restrained by Local Information YANG Ling 2 HOU Xiao-Ye 2 WANG Zhong-Ke 3 RAO Ni-Ni * School of Life Science and Technology University of Electronic Science and Technology Chengdu China 2 College of Electronic Engineering Chengdu University of Information Technology Chengdu China 3 College of Network Engineering Chengdu University of Information Technology Chengdu China Abstract In CT images the mandibular canal is difficult to distinguish from other surrounding tissues due to its tubular structure. This paper proposed a shape-driven level-set algorithm restrained by local information to segment the mandibular canal with high accuracy. Firstly according to the location of the distribution of the mandibular canal we reconstructed many cross sectional images of corresponding parts of the mandibular canal and extracted the mandibular canal from the cross sectional images and then applied principal component analysis PCA to carry out the shape priori statistics of the mandibular canal. Finally based on shape-driven level-set algorithm restrain the evolution of the level-set energy function to improve the segment result in the fuzzy region by introducing the local information of the mandibular canal s surrounding tissue. By applying the criterion of segmentation accuracy in the region of interest the segmentation accuracy of shape-driven level-set algorithm was 4. 9% while the segmentation accuracy of our method is. 82%. Experimental results showed that this method could effectively segment patients mandibular canal and provide an effective method for locating the position of the mandibular canal. Key words CT image mandibular canal shape prior shape-driven local information doi / j. issn FZ0034 * raonn@ uestc. edu. cn

2 62 3. CT 2 CT 3DX MULTI-Image Micro CT CT AC00 V 2. 0 kva X 0. 5 mm 0. 5 mm s 40 mm 30 mm mm mm mm 260 CT Tamas 6 X Obradovic CT CT 9 CT 8 a B a Materialise Simplant a CT n p = S y p- S x p 槡 S x p S y p CT S x p S y p B S p p x y 260 CT Yau b 9 Bresson Mumford-Shah 0 CT a CT b PCA Fig. CT image of the mandibular. a CT axial image b The mandibular cross sectional image

3 Ω i Ω o Φ * g g k α Φ * α = exp - F n = F o + β l F l C f x f 2 x 9 ( 2π 槡 k A 2 α T A - k α) 4 β l F l k A n n F A k A k l C f x f 2 x = λ k x - y Ι y - f x 2 dxdy + Ω i Bresson 0 λ 2 ( k x - y Ι y - f 2 x 2 dxdy Ω o ) 0 k σ u = 2π n /2 σ e - u 2 /2σ2 y n F o = β s F s C α X T + β b F b C + β r F r α X T u i u o 5 x - y > 3σ F s F b F r x F s = 0 Φ * 2 α X t C q C' q dq F b = g I C q C' q dq F r 0 = Ω0 Ι - u 2 i + μ 2 u i dω + a X T 6 7 Ι - u 2 o + μ 2 u 0 dω 8 Ω0 a X T C I x y X t X T 4 [ 0 + ) R + g( 0 ) = g( x) 0 x β s β b β r principle component analysis PCA Φ Φ 2 Φ n 珡 Φ = / nσ n Φ i = i 2 U U 2 U n n Φ Φ 2 Φ n U i Σ = / n MM T SVD M n Φ * = U k α + Φ 3 U k U k α k 2 ( ) x f x f 2 x x x x' x' = x + Δx x x' f' x f' 2 x f x f 2 x 3 f x f 2 x x * H Φ x Ι x f x = k σ k σ x * H Φ x

4 64 3 f 2 x = k σ x * - H Φ x Ι x k σ x * - H Φ x 2 * H xheaviside Φ t = δ Φ β s F s - 2β b F b + β r F r + β l F l δ x = H' x 3 3 λ = λ 2 = n n n > 4σ σ σ = 3 n = a 2 OsinX b c b 0 dc 0 3 a Fig. 3 Extraction fo the average contour of the 3 b mandibular canal. a The mandibular cross sectional image Rectangular area is the initial contour b 20 Binary sample of the mandibular canal c The 3 c average contour of the mandibular canal dark line 3 d and training shapes light line d The enlarged SDF = 3 a image of figure c 's center region mu0 = /. tx0 = ty0 = a ~ e 2 ~ k ~ f ~ j k ~ o o f ~ j a ~ e f ~ j 4 fk β s = 0. β b = 0. 2 β r = 4 β l = 0. 0 μ = 50 Δt = f 4 k 2 OsinX 5 Fig. 2 Panoramic image in OsinX software

5 a ~ e 2 ~ 5 f ~ j a ~ e k ~ o f ~ j Fig. 4 The first patient s segmentation contour solid line and shape prior dotted line. a ~ e The mandibular cross-sections at the position ~ 5 in Fig. 2 respectively f ~ j The local enlarged images corresponding to a ~ e respectively k ~ o The segmentation results without local information corresponding to f ~ j respectively 5 2 a ~ e 2 ~ 5 f ~ j a ~ e k ~ o f ~ j Fig. 5 The second patient s segmentation results contour solid line and shape prior dotted line. a ~ e The mandibular cross-sections at the position ~ 5 in Fig. 2 respectively f ~ j The local enlarged images corresponding to a ~ e respectively k ~ o The segmentation results without local information corresponding to f ~ j respectively

6 M f 4 5 a ~ e0 M f M f M f = R f - S f R f S f. 82% R f R f 4. 9% R f - S f R f S f R f - S f Tab. The accuracy comparison results of image segmentation 4 5 a b c d e a b c d e / % / % J Bu Lingxue Wang Ke Chen Xin et al. Anatomic structure of. the mandibular canal J CT J 24. images for dental implant surgery J and Applications Yau HT Lin YK Tsou LS et al. An adaptive region growing method to segment inferior alveolar nerve canal from 3D medical. Computer-Aided Design 0 Bresson X Vandergheynst P Thiran JP. A variational model for object segmentation using boundary information and shape prior driven by the mumford-shah functional J. International Journal of Computer Vision Leventon M E Grimson WEL Faugers Q. Statistical shape Pogrel MA. Damage to the inferior alveolar nerve as the result of influence in geodesic active contour C / / IEEE Conference of root canal therapy J. JADA Computer Vision and Patten Recognition. Hilton Head Island 2 Gyehyun K Jeongjin L Ho L et al. Automatic extraction of IEEE inferior alveolar nerve canal using feature-enhancing panoramic volume rendering J. IEEE Trans on Technol Biomed Li Chunming Kao CY Gore JC et al. Implicit active contours driven by local binary fitting energy C / / IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis IEEE 3 Tamas F. Position of the mandibular canal J. J Oral Maxillofac Surg Wang Li Li Chunming Sun Quansen et al. Brain MR image 4 Obradovic O Todorovic L. Morphometric analysis of mandibular segmentation using local and global intensity fitting active canal clinic aspects J. Bull Group Int Rech Sci Stomatol contours / surfaces C / / th International Conference on Odontol Medical Image Computing and Computer-Assisted Intervention. 5. New York Springer J Pardo XM Carreira MJ Mosquera A et al. A snake for CT 6 Pu Tiemin Zhu Xinyi Li Jianfeng et al. Multi-section image segmentation integrating region and edge information J. measurements of mandibular canal of adult ex vivo mandibles Image and Vision Computing

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