3 2 202 4 Chinese Journal of Biomedical Engineering Vol. 3 No. 2 April 202 2 2 3 * 60054 2 60225 3 60225 CT PCA 4. 9%. 82% CT TP39. 4 A 0258-802 202 02-06-06 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 60054 China 2 College of Electronic Engineering Chengdu University of Information Technology Chengdu 60225 China 3 College of Network Engineering Chengdu University of Information Technology Chengdu 60225 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 0. 3969 / j. issn. 0258-802. 202. 02. 00 20-09-7 202-02-05 20FZ0034 * E-mail raonn@ uestc. edu. cn
62 3. CT 2 CT 3DX MULTI-Image Micro CT CT AC00 V 2. 0 kva X 0. 5 mm 0. 5 mm 360 8 s 40 mm 30 mm 480 0. 25 mm 0. 25 mm 0. 25 mm 260 CT 683 683 2 987 Tamas 6 X 3 993 Obradovic. 2 4. 2. CT 5-7 60 CT 9 CT 8 a B a Materialise Simplant a CT n p = S y p- S x p 槡 S x p 2 2 + 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
2 63. 2. 2 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 =. 2. 3 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
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 = 3 2 3 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 35 65 35 65 = 3 a image of figure c 's center region mu0 = /. tx0 = - 3 2 ty0 = - 4 5 a ~ e 2 ~ 5 4 5 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 = 0. 4 7 4 f 4 k 2 OsinX 5 Fig. 2 Panoramic image in OsinX software
2 65 4 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
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