Automatic Knee Cartilage Segmentation Using Multi-Feature Support VectorMachine and Elastic Region Growing for Magnetic Resonance Images

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Automatic Knee Cartilage Segmentation Using Multi-Feature Support Vector Machine and Elastic Region Growing for Magnetic Resonance Images

Article in Journal of Medical Imaging and Health Informatics · August 2016

DOI: 10.1166/jmihi.2016.1748

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Journal of Medical Imaging and Health Informatics Vol. 6, 1–9, 2016

Automatic Knee Cartilage Segmentation Using Multi-Feature Support Vector Machine and Elastic

Region Growing for Magnetic Resonance Images

Pin Wang¹^∗, Xuan He¹, Yongming Li¹², Xueru Zhu¹, Wei Chen³, and Mingguo Qiu²^∗

1College of Communication Engineering of Chongqing University, Chongqing, 400030, P. R. China

2Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, P. R. China

3Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, 400038, P. R. China

In this paper, a method of segmenting cartilage region from knee magnetic resonance imagesin vivois presented. The scheme involved bone-cartilage interface edges location and elastic region growing. Firstly, the bone edges were obtained by modified adaptive canny edge detection and multi-feature support vector machine classifier. The accurate bone-cartilage interface edges were achieved by optimizing the bone edges. Subsequently, the seeds and candidate regions were chosen based on the bone-cartilage interface edges and the preliminary segmentation of articular cartilages were achieved by using the elastic region growing. Finally, the anatomic knowledge and morphology were used to improve the primary segmentation results. Anatomical knowledge was used to distinguish the overlapped cartilage parts, and morphology was used to optimize the results. The proposed automatic segmentation method shows good consistency with manual segmentations and achieved average dice similarity coefficient values (0.851, 0.826, 0.857) for (femoral, tibial, and patellar) cartilages.

Keywords: Knee Cartilage, Multi-Feature Support Vector Machine, Elastic Region Growing, Automatic Segmentation, MRI.

1. INTRODUCTION

The knee joints are the most complicated and easily damaged joints in human body. The common diseases of the knee joints include osteoarthritis (OA), and bone tumors etc.¹For those diseases, the knee joint cartilage usually has degeneration, damage and morphological change. In order to properly assess the damage to cartilage, quantitative cartilage measurement is required, for which the segmentation is the essential task.²

The magnetic resonance imaging (MRI) is an effective imaging technique that capable of producing high resolution, high-contrast images in serial contiguous slices. It is a kind of noninvasive medical test and has become the main method to assess cartilage morphology and function.³⁴ Currently, quan- titatively measurements (e.g., thickness and volume) of MRI articular cartilage are technically demanding. The accuracy of segmentation of articular cartilage has a significant effect on the percentage errors and reproducibility of the quantitative measurements. The segmentation of cartilage tissue at present is implemented by physicians manually.^5–8These methods are labor intensive, prone to error and subject to subjective judgment of an observer.

∗Authors to whom correspondence should be addressed.

A variety of techniques have been applied for semi- automatically cartilage segmentation. Snake algorithm has been introduced for this application, and proposed many additional snakes to improve the segmentation performance.^9–11 However, the snake has some limitations in convergence, the optimization problem involved leads to uncertainty and poor stability of the result of segmentation. Model-based methods were proposed for the automatic segmentation by formulating the priori knowledge of the cartilage.^12–14Yin et al. used LOGISMOS (layered optimal graph image segmentation of multiple objects and surface) framework to segment the bone and cartilage surfaces.¹⁵ Fripp et al.

developed a hierarchical segmentation scheme that involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using deformable model.¹⁶Shan et al. automatically segmented femoral and tibial cartilage, proposing a multi-atlas segmentation strat- egy with non-local patch-based label fusion which can robustly identify candidate regions of cartilage.¹⁷ Model-based methods highly rely on the datasets used and thus it is not easy to be reproduced. Image segmentation can also be treated as a statistical classification problem where each voxel belongs to a specific class. Folkesson et al. presented a two step k-nearest neighbor

J. Med. Imaging Health Inf. Vol. 6, No. 4, 2016 1

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J. Med. Imaging Health Inf. 6, 1–9,2016 (k-NN) voxel classifier to automatically separate cartilage from

non-cartilage.¹⁸Zhang et al. proposed an automatic cartilage segmentation method which exploits a rich set of image features from multi-contrast MRI and the spatial dependencies between neighboring voxels.¹⁹Pang et al. proposed an automatic method using three binary classifiers with integral and partial pixel features based on Bayesian theorem.²⁰The computational complexity is relatively high for the voxel-based classification methods.

In this paper, a new automatic articular cartilage segmentation method based on multi-feature support vector machine and elastic region growing (MFSVM-ERG) is proposed. The method automatically recognizes the bone-cartilage interface (BCI) based on the multi-feature support vector machine (MFSVM) and bone edges optimization. Based on BCI edges, the elastic region growing (ERG) is applied to achieve the cartilage segmentation results.

Although BCI recognition¹⁶ and SVM¹⁹ have been used for cartilage segmentation, there are significant differences between this study and those works:

(1) we combine MFSVM and ERG for cartilage segmentation;

(2) the work in Ref. [16] used active shape model for BCI recognition, our method achieves the BCI by using MFSVM;

(3) MFSVM had been used in Ref. [19] for the pixel classification, the proposed algorithm segments the cartilage based on BCI rather than pixels directly.

(4) Region growing is widely used in the image segmentation.²¹²²

ERG can adaptively adjust the process of region growing and has strong robustness. We specialize it for cartilage segmentation.

The rest of the paper is organized as follow. Section 2 presents MRI acquisition of the knee and provides the detail information of the MRI. Section 3 describes the MFSVM-ERG segmentation method which includes the BCI edge location and cartilage segmentation. Section 4 presents the experiment results of the proposed articular cartilage segmentation scheme and compares its performance with other methods. A discussion and conclusion of this work is given in Section 5.

2. MR IMAGE ACQUISITION

In this paper, the MR images are acquired from right knee joint of eleven healthy males (mean age: 23.5 years; range:

20–25 years). Written consent was obtained from each participant, and the Ethics Committee of the Third Military Medical University approved the protocol. The MRIs were acquired by a 1.5 T MR scanner (Avanto, MAGNETOM, Siemens). The param- eters are listed as following: echo time (TE) 4.42 ms, repetition time (TR) 1363 ms, scan Time 4–5 minutes, flip angle (FA) 60, slice thickness 2.5 mm, slice gap 1 mm, NEX-Number of acqui- sitions 2 times, field of view (FOV) 160 mm, and matrix size 384×384. Figure 1 shows that the MRI can clearly show the patellar cartilage, femoral cartilage and tibial cartilage.

As shown in Figure 1, one side of the patellar cartilage (PC), femoral cartilage (FC) and the tibial cartilage (TC) connect with the patellar bone, tibial bone and femoral bone. There are obvious gray differences between them. But the other side of the patellar cartilage and femoral cartilage are linked together. The femoral cartilage connect with the tibial cartilage, meniscus and the adipose, the gray values of these parts are consistent. The cartilage is thin and flat, so the segmentation is easily to be affected

Fig. 1. The original MRI image of knee.

by the complex texture around. Figure 2 shows high degree of overlap between these tissues’ intensity distributions. It makes the desired joint structures, like bones and cartilages, difficult to be separated from other joint structures based solely on the threshold or Canny edge detection.

3. SEGMENTATION METHOD

Based on the characteristics of the MR images of knee articular cartilage, the framework of the algorithms is shown in Figure 3.

The method is divided into two parts:

(1) Locating the BCI based on the adaptive canny edge detec- tor and MFSVM. Firstly, the coarse edges were detected by a modified adaptive canny operator. The two different thresholds (high thresholdT_hand low thresholdT_l) of canny edge operator were adaptively calculated by iterative algorithm. Secondly, each edge was represented by a high-dimensional feature vector con- sisting of local structures, geometrical information of anatomical structures, and neighboring voxels information. Subsequently,

Fig. 2. Normalized intensity histograms of cartilages and the surrounding tissues of the MR Images shown in Figure 1.

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Fig. 3. Flow chart of the proposed automatic segmentation method.

the bone edges were recognized by one-versus-one (1-v-1) SVM based on multiple features. Since the BCI is a part of the bone edges, the accurate BCI edges were obtained after optimizing the bone edges.

(2) Segmenting the cartilages based on the BCI and ERG. The seeds and candidate regions were automatically selected based on the BCI edges. An ERG algorithm with the updated similarity criteria was used to achieve the preliminary segmentation of articular cartilages. Subsequently, the accurate segmentation of FC, TC and PC were obtained by optimizing the primary segmentation results using the anatomic knowledge and morphology.

3.1. BCI Edge Location

3.1.1. Adaptive Canny Edge Detection

Since some noise exist in the MRI, particularly in the bone tissue, which would severely affect edge detection, it was necessary to be denoised. A Gaussian low-pass filter was used to denoise the MRI, where the size of filter was 5×5, and the standard deviation was 2.

The traditional edge detection methods can be divided into the edge detection based on the gray-level histogram and the edge detection based on the gradient.²³ The threshold edge detection needs to select the appropriate threshold value which will greatly affect the edge detection result. When the gray level histogram appears to be bimodal, the result of detection is good. But the histogram of knee MRI sequences is not bimodal and the gray is evenly distributed, so the threshold edge detection cannot achieve satisfying results. The gradient edge detection is based on the gray mutation in the image edge, using the pixel gradient value to distinguish the edge points and the non-edge points. The com- monly used edge detection operators are Sobel, Prewitt, Robert, Log, Canny, etc. Compared to other operators, canny operator has the advantage of using two different thresholds (high threshold T_h and low threshold T_l) to detect coarse edges and fine edges respectively. Only if the weak edge is connected to the strong edge, it can be included in the output image. In this experiment, canny operator is chosen for edge location of the image sequence.

The traditional canny operator needs to manually set the T_h and T_l. However, the number of the MRI slices of each patient is more than 30, manual setup is time-consuming.

In order to improve the efficiency and generalization, the

adaptive canny operator was applied to select the T_h and T_l dynamically. The modified adaptive canny edge detection algorithm is described as follows:

Step1: Compute gradient magnitude from the Gaussian smooth- ing image with non-maximum suppression (NMS).

Step2: Adopt the iterative algorithm²⁴ to calculate the global threshold Tgrad of gradient magnitude, the mean magnitude of high gray-level region_h, the mean magnitude of low gray-level region_l, the variance of two regions_hand_l. TheT_handT_l is set as follows:

⎧⎨

⎩

T_h=_h+_h×_h

T_l=_l+_l×_l (1) Step3: Detect the coarse edges using the Canny operator with T_hand T_l. Adaptively adjust the values_hand _lbased on the number and length of edges to obtain a better edge detection result. If the number of edges is too high, the values of_hand_l should be reduced. If the number of edges is too low, the values of_hand_l should be risen.

Step4: Optimize the detecting edges. In order to improve the accuracy of BCI recognition, the detecting edges (Figs. 4(D–F)) were optimized by detecting the corners using Harris operator.

According to the anatomy and geometry, the corners of edges were detected and processed based on multiple strategies. The corners of patellar edges (as shown in red mark) were broken before classification, and the corners of femoral edge and tibial edge were processed after classification. A candidate of bone edges was obtained (Figs. 4(G–I)).

The results of adaptive canny edge detection are shown in Figure 4, where (A–C) are the original MRIs. Figures 4(D–F) are the coarse results of adaptive canny edge detection.

Figures 4(G–I) is the optimization results of (D–F).

From the figure above, it can be seen that the adaptive edge detection algorithm can obtain the satisfying edge. Compared

Fig. 4. The results of adaptive Canny edge detection. (A–C) is the original MR image. (D–F) is the coarse results of adaptive Canny edge detection.

(G–I) is the optimization results of (D–F).

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J. Med. Imaging Health Inf. 6, 1–9,2016 with the others edge operators, this algorithm can detect the main

edges which include the edges of the cartilages. Besides, this algorithm can detect the edges’ corners, break them and remove the parts which do not belong to the cartilages as shown in the red boxes in the Figures 4(H and I).

3.1.2. Feature Extraction of Located Edge

As shown in the Figures 4(G–I), in addition to the real bone edges, the adaptive canny edge detection operator may also detect some false edges, so it is necessary to identify these false edges and remove them. The pattern classification method is proposed in this paper to identify the detected edges, thus completely obtaining the real bone edges.

First, mark the edges that had been detected, and extract the features for each edge. Three kinds of features (local structures, geometrical information of anatomical structures, and neighboring voxels information) were selected to classify the true edges and the false edges. The features are listed in Table I. As seen in the Table I, the features 1 to 8 are the features based on local structures; the features 9 to 17 are the features based on geometrical information of anatomical structures; the features 18 to 20 are the features based on neighboring voxels information. The extracted features can describe the characteristics of the edges more comprehensively.

Table I. Features of edge.

No. Feature equation Feature description

1 n The pixel number of the edge line

2 f¯=

x y⊂L

fx y

/n Average grey value of the line,L: the edge line;f: the grey value of original image;n: the pixel number of the edge line

3 v=varfx y x y⊂L Gray value variance of the edge points

4 g¯=

x y⊂L

gradfx y

/n Average gradient value of the edge points

5 x¯=

x y⊂L

x

/n AverageXcoordinate.x:xcoordinate of the edge points

6 y¯=

x y⊂L

y

/n AverageY coordinate.y:ycoordinate of the edge points

7 v_x=varx x y⊂L Variance of theXcoordinate

8 v_y=vary x y⊂L Variance of theYcoordinate

9 w=maxx−minx Horizontal projection

10 h=maxy −minx Vertical projection

11 numleft=

x y⊂L

A y < Ax y The number of the points at the left of the edge.A: all of the edge;Ax y : the edge of being extracted features

12 numright=

x y⊂L

A y > Ax y The number of the points at the right of the edge

13 opendirect=maxT_up T_down T_collinear Opening direction.T_up,T_down,T_collinear: the ratio of up, down and collineation respectively

14 angle=absangle₁−angle_end Clockwise rotation angle. angle₁, angle_end: the angle between thex-axis and initial point, terminal point respectively

15 angdirect=

⎧⎨

⎩

1 angle<180

−1 angle>180

Direction of rotation

16 numhori=Freeman1+Freeman5 Number of the pixel in the horizontal direction. Freeman(1): the number of edge points which direction of Freeman Code is 0; Freeman(5): the number of edge points which direction of Freeman Code is 4

17 ledgeman=

¯

x_A<x¯ The number of the edges at the left of the edge of being extracted features

18 f¯=

x y⊂L

fx y

n₅ Average of gray values in the 5 neighborhood of the points.L₅: 5 neighborhood;n₅: the number of the 5 neighborhood points of the edge

19 v₅=varfx y x y⊂L5 Variance of gray values in the 5 neighborhood of the points

20 v¯=

x y⊂L

var₅fx y

n The mean variance in the 5 neighborhood of each point on the edge

3.1.3. Edge Classification Based on MFSVM

SVM is a machine learning algorithm whose goal is to find an optimal separating hyperplane to accurately classify all of the training set. In SVM, the linearly inseparable samples are mapped into a high dimensional feature space. Therefore, the problem is converted to solve a convex optimization in feature space. Let x_i y_ii=12 N be a training set, where x_i is a feature vector,y_i is a classify label ofx_i,N is the number of training samples. The model of the MFSVM is presented as following:

⎧⎪

⎪⎪

⎪⎨

⎪⎪

⎩ max

i

_i−1 2

i j

_i_jy_iy_jKx_ix_j

constraint condition 0≤_i≤C i=12 N N

i=1

_iy_i=0

(2)

f x=^N^s

i=1

_iy_iKx_ix+b (3)

wherex is the testing set,bis scalar which can be learned, x_i is a support vector, N_s is the number of the support vectors.

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We adopt the Gaussian radial basis function (RBF) as kernel for classification

Kx_i x_j=exp

−x_i−x_j² ²

(4) The goal is to classify four types of edges (femoral edges, tibial edges, patellar edges and other edges). It’s a multi- classification problem which can be solved by one-versus-one (1-v-1) approach.²⁵1-v-1 methods require structuringkk−1/2 binary classifier, wherekis the number of classification. Using

’voting mechanism’ calculate the typeiof testing samplex.

C_x=arg max k j=i j=1

sgnf_ijx (5)

where j is the number of classifier, C_x is the vote number of type, iof testing samplex, sgnf_ij is the sign function (f_ij>0, sgnf_ij=1 f_ij <0, sgnf_ij=0). If the voting number of more than one type is the same, these types will be voted again using 1-v-1 methods until the voting number is not the same.

As discussed above, in order to classify edge into femoral edges, tibial edges, patellar edges and other edges, six classifiers were needed to be built. To validate the effectiveness of the extracted features, different features vector (or feature sub- set) was used to classify the 2782 edges of eleven people. Three different feature vectors and the corresponding recognition rate are shown in Table II. FV1 consists of 8-dimensional local structures based features, FV2 consists of 9-dimensional geometrical features of anatomical structures, and FV3 consists of all the 20-dimensional features. All the experiments were performed using a leave-one-out cross-validation approach. The particle swarm algorithm was used to optimize the SVM penalty param- eterC and kernel parameter. The recognition ratesAccallow us to visually observe the benefits of using multi-features. The Accis defined as follow:

Acc= TP+TN

TP+TN+FN+FP (6)

Where TP was true positive, TN was true negative, FP was false positive, FN was false negative. It can be noted that results obtained using FV3 give the highest recognition rate and can identify almost all of cartilage edges.

3.1.4. BCI Recognition

Although the process above can recognize the bone edges cor- rectly, only a part of the bone edges belonged to the BCI. Thus, in order to identify the true BCI edges, the bone edges optimization was needed. According to priori knowledge, considering the

Table II. Edge recognition rate with different feature vector.

Features vector Dimension Features Recognition rate

FV1 8 Features 1∼8 Femur edges 0.921

Tibia edges 0.913 Patella edges 0.675

location and distance between each bone, the false points of bone edges could be removed. The accurate BCI edges were obtained by optimization steps as follows:

Step1: In the case of the femur and patella adhesions (Fig. 5(B)), the points do not belong to patellar BCI needed to be removed. Based on anatomy, the patellar BCI should be located at the left of the femoral BCI.x_p, y_p and x_f, y_f represent the X and Y coordinate of patella and femur bone, respectively. If x_p =x_f and y_p> y_f, the point should be removed from the patella bone edge. Consequently, we obtain the accurate patellar BCI edge_pas shown in Figure 5(E).

Step2: Harris operator was used to break the corner of femoral edge and tibial edge. For the femoral edge (Figs. 5(A, C)), the right corner divided it into two parts. The cartilage only covered the lower one. Thus, the upper part was removed to get the accurate BCI of FC (Figs. 5(D, F)). For the tibial edge (Figs. 5(A–C)), the corners divide it into two or three parts. The cartilage only covered the upper part. Hence the upper part was retained to be the BCI of TC (Figs. 5(D–F)). Consequently, the accurate femoral BCI edge_f and tibial BCI edge_t were obtained.

The results of bone edges and BCI are shown in Figure 5.

Figures 5(A–C) are the results of bone edges based on MFSVM;

Figures 5(D–F) are the results of accurate BCI. The red 1 means edge_f; the red 2 means edge_t; the red 3 means edge_p. As shown in Figures 5(D–E), edge_f, edge_f and edge_f represent the accurate BCI.

3.2. Cartilage Segmentation 3.2.1. Elastic Region Growing

Region growing is a typical serial region segmentation method, and the following steps depend on the previous results. Since the target for processing is pixel, considering the time cost issue, the region growing is a satisfying method compared with other methods such as classification. The process steps of region growing are shown as follows: Firstly, one or more seed points are selected as the starting point for region growing. Secondly, the pixels in the adjacent areas are compared with those of the seeds.

If they meet similarity criteria for growth, the points will be put into the seeds area. Thirdly, the second process is repeated until all the pixels are scanned. Finally, the region growing

Fig. 5. The results of MFSVM and optimization. (A–C) are the results of bone edges of MFSVM; (D–F) are the results of accurate BCI. The red 1 is edge_f; the red 2 is edge_t; the red 3 is edge_p.

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J. Med. Imaging Health Inf. 6, 1–9,2016

Fig. 6. The comparisons of the segmentation results using different methods. Column 1 are the results of radiologist’s manually segmentation, column 2 are the results of the Pang et al.’s automatic method, column 3 are the results of the proposed method MFSVM-ERG.

algorithm ends. The similar features can be gray scale, boundary information, or a combination of both.

The key of region growing is as follow:

(1) Select seed points. The seeds of traditional region growing method are selected manually. It is time consuming and not stable. In order to improve the efficiency, ensure stability and avoid human interference, the proposed elastic region growing (ERG) automatically select seeds based on the BCI.

(2) Determine similarity criterion. Similarity criterion of traditional regional growth is mainly based on global gray difference. Similarity criterion is defined based on the difference between the gray value of candidate growing points and that of the global image. However, the cartilages are much smaller than

Fig. 7. Segmentation results of MFSVM-ERG for (A–D) DESS and (E–H) TSE. (A–B), (E–F) are the participant with no obvious signs of osteoarthritis.

(C–D), (G–H) are the participant with signs of osteoarthritis.

other tissues, and the gray difference between the bone and cartilages is larger, the mean gray value of globalmcannot represent the gray value of cartilage. Therefore, the local gray value of grown regionm is taken instead of the globalm.

(3) Determine the terminal condition.

The steps of the ERG are as follows:

Step1: Automatically select seeds base on BCIs. In this study, the cartilage of the knee is narrow, elongated and curved. The cartilage distributed on a certain range of the bone surface, the thickness is within 1∼6 mm (3∼20 pixels in the images). Candi- date seeds of cartilage (FC, TC and PC) are restricted within 20 pixels from the BCI. Find the largest connected areaf with small variance in the gradient magnitude. Seed points seed_ii=123 were randomly selected in field_ii=123that covers from the outer border of f to the BCI edge, where i is the category of cartilage, seed₁ is the seeds of FC, seed₂ is the seeds of TC, seed₃is the seeds of PC.

Step2: Determine whether met the similarity criterion. The local gray of grown regionm instead of the global graymwas taken.

The similarity criterion was defined as follow:

⎧⎪

⎪⎨

⎪⎪

⎩

f x y−m< T m = 1

n

x y∈R

f x y (7)

where R is the new local region along with the changes of region growing,nis the number of pixels after growing region, m is the mean gray value of growing region.T is the threshold.

When the difference between the gray value of the pixelf x y and them was smaller thanT, then the pixel was put into the growth area. The fixed setting ofT affected the generalization, so T was automatically set as the standard deviation of growing region:

= 1

n N x y∈R

f x y−m² (8) Step3: Update similarity criteria. Calculate the average gray value of the area that had been grown, and update criteria for

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Table III. The mean DSC, sensitivity, specificity with different methods.

Works Cartilage Mean DSC Mean specificity Mean sensitivity Pang FC 0.8042±0.0463 0.9984±0.0008 0.8377±0.0505 et al.²⁰ TC 0.7259±0.0403 0.9988±0.0005 0.7859±0.0748 PC 0.7002±0.2397 0.9082±0.3012 0.8349±0.2894 MFSVM- FC 0.8509±0.0489 0.9991±0.0004 0.8519±0.0814 ERG TC 0.8261±0.0517 0.9992±0.0003 0.8716±0.0543 PC 0.8572±0.0359 0.9996±0.0002 0.8790±0.0568

region growing. Take the new growth points as the seed points and proceed to Step 2.

Step4: Determine the termination conditions. If there is no new point meeting the similarity criteria, the region growing algorithm ends, otherwise turns to Step 2.

3.2.2. Cartilage Segmentation Optimization

Since the ERG algorithm was applied for FC, TC and PC respectively, the segmentation results of cartilage may be overlapping.

In order to improve the accuracy of the segmentation results, the optimization was needed after ERG. Based on knowledge of anatomy, overlapping possibly appeared between the left side of FC and the right side of PC, and between the downside of FC and the upside of TC. The Euclidean distance between the overlapped points and corresponding BCIs was calculated. The point that belonged to the BCI had shorter Euclidean distance. Finally, the segmentation result of cartilage was obtained by using morphology. The segmentation results are shown in Figures 6(C, F, I).

4. EXPERIMENT RESULTS AND ANALYSIS

4.1. Evaluation Metrics

To evaluate the performance of MFSVM-ERG algorithm, two evaluation ways were applied, including visual evaluation and quantitative evaluation. In the visual evaluation, the 2D slices of manual segmentations and the MFSVM-ERG segmentations are shown in Figure 6, and their differences can thus be directly per- ceived. In the quantitative evaluation, Sensitivity, Specificity and Dice similarity coefficient (DSC) were calculated. The Sensitiv- ity, Specificity and DSC are defined as follows:

Sensitivity= TP

TP+FN (9)

Table IV. Comparison of different automatic algorithm for knee cartilage segmentation.

Mean DSC for Segmentation

Methods System configuration Subject time FC TC PC All

Layered optimal graph¹⁵

Intel Core 2 Duo 2.6 GHz processor and 4 GB of RAM

One knee joint dataset of OAI using DESS squence

20 minutes 0.840 0.800 0.800 0.813 Hybrid deformable

model¹⁶

– One knee joint using FS

SPGR MR squence

15 minutes 0.848 0.826 0.833 0.836 Atlas-based three

label¹⁷

– An MR image Hours 0.856 0.859 – 0.857

k-NN classification¹⁸ Standard desktop 2.8 GHz PC One knee joint 10 minutes 0.770 0.810 – 0.790

SVM-DRF¹⁹ A 48-core high performance computer with Linux system

One knee joint 13 minutes 0.860 0.880 0.840 0.860

Pattern recognition²⁰ Pentium dual-core 3.2 GHz processor and 2 GB of RAM

One knee joint with 30∼42 slices

8 minutes 0.804 0.726 0.700 0.743

MFSVM-ERG Pentium dual-core 3.2 GHz processor and 2 GB of RAM

One knee joint with 30∼42 slices

7 minutes 0.851 0.826 0.857 0.845

Sepicficity= TN

FP+TN (10)

DSC= 2×TP

2TP+FP+FN (11)

Where TP was true positive, TN was true negative, FP was false positive, FN was false negative. DSC represented the overlap between the manual segmentation result and the automatic segmentation result. DSC ranges between 0 and 1, where 1 meant complete overlap, 0 means no overlap.

4.2. Experimental Results

Several experiments were organized to verify the proposed method. Experimental platform was based on PC with 3.2 GHz, the programming language was Matlab 2010b. The time of automatic segmentation of 30∼42 slices was approximately 7 min while the manual segmentation was approximately 30 min. The proposed method was compared with different automatic methods for knee cartilage segmentation. In addition, the experiment results were compared with the radiologist’s manual segmentation results. The radiologist has 8 years clinical experience and is responsible for manual segmentation.

Figure 6 shows the segmentation results of the different slices of the person’s MRI using radiologist’s manually segmentation, the Pang et al.’s automatic method, and the proposed method (MFSVM-ERG). Each row is segmentation results of the same slice and each column is the segmentation results using the certain method. Column 1 are the results of radiologist’s manually segmentation, Column 2 are the results of the Pang et al.’s automatic method,²⁰Column 3 are the results of the proposed method MFSVM-ERG. Comparing Figures 6(B, C and A), the FC is segmented more accurately using the proposed method. Comparing Figure 6(D) with Figures 6(E, F), the method of Pang et al. does not segment the PC due to the missing of patellar BCI. Com- parison Figures 6(H, I) with the manual result, the segmentation result of the proposed method is more accurate. From a qualita- tive point of view, the cartilage tissue segmented by MFSVM- ERG method match well with the manual segmentation results.

The quantitative evaluation using different automatic methods for 369 MRI slices of 11 persons is shown in Table III.

It can be seen that compared with the Pang et al.’s approach, MFSVM-ERG has significant improvement. Compare with manual segmentation, the results have good consistency. The average

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R E S E A R C H A R T I C L E

J. Med. Imaging Health Inf. 6, 1–9,2016 value of the DSC is (0.851, 0.826, 0.857) for the (FC, TC, PC).

The results of the sensitivity and the specificity indicate that the proposed method has good segmentation performance. The segmentation method is automatic, so it do not subject to subjective judgment of an observer. It can be applied for cartilage segmentation of different slices and different people, the segmentation speed and the accuracy is high and stable.

In order to validate the proposed method for other dataset, the experiment was carried out on the publicly available Osteoarthri- tis Initiative (OAI) database, which is available for public access at https://oai.epi-ucsf.org/datarelease/. The OAI database contains the cases with different sequence and different levels of knee joint degeneration. The proposed method MFSVM-ERG was applied on two participants from OAI. One participant has no obvious signs of osteoarthritis, and the other participant have signs of osteoarthritis. The sequences of MRIs are sagittal 3-D dual-echo steady state (DESS) with water excitation (about 160 slices of one knee) and sagittal intermediate weighted turbo spin echo (TSE) with fat suppression (about 37 slices of one knee). The segmentation results are shown in Figure 7. It can be seen that the algorithm can work well on different MR pulse sequence images.

5. DISCUSSION AND CONCLUSION

In recent years, a number of automatic cartilage segmentation works have been reported by using statistical or deformable model, atlas registration, and pattern recognition.^15–20 Most of these approaches particularly highly rely on the datasets used and thus it is not easy to reproduce them. The proposed method (MFSVM-ERG) does not rely on such priori information which related to the volunteer demographics and pathological state of the knee. The BCI edges location based on MFSVM was used to limit the ERG. The local structures, geometrical information of anatomical structures, and neighbor voxel information form MRI were taken as the features to achieve more robust cartilage segmentation. Once the articular MFSVM-ERG model was trained, it can be used to automatically segment cartilages from healthy or pathological subjects.

Table IV shows the comparison of different automatic algorithms for cartilage segmentation. It can be observed that the proposed methods outperform the works¹⁵¹⁶¹⁸²⁰ for automatic segmentation of FC, TC and PC in terms of the average DSC value. The average DSC obtained for all the cartilage using SVM-DRF¹⁹ (0.86) and atlas-based three label¹⁷ (0.857) was slightly higher than our approach (0.845). However, the SVM- DRF is a voxel-based classification method and relies on the features from multi-contrast MRI. The computational complexity is relatively high. The atlas-based three label algorithm espe- cially relies on prior information and training datasets from both healthy subjects and patients with OA. So their generalization capability cannot be guaranteed well. The proposed algorithm extracted three different types of features from MRI to locate the BCI edges, which improved the computational efficiency and provides more steady performance. Moreover, elastic region growing based on the location of BCI can assure more robust cartilage segmentation. Besides, seen from in the Table IV, compared with other methods the proposed algorithm has the low time cost, which is important for automatic cartilage segmentation.

In this paper we designed a hybrid automatic segmentation algorithm, which combines multi-feature SVM and elastic region growing. The automatic segmentation results show good consistency with the results of manual segmentation. Compared with other automatic segmentation algorithm, the proposed method was effective and had advantages as follows:

(1) multi-feature SVM was used for edge detection to improve the accuracy of BCI edge location;

(2) elastic region growing rather than voxel-based classification was applied to balance the accuracy and time cost.

Although this method was verified through hundreds of slices of eleven people and different MR pulse sequence images from OAI dataset, it was still necessary to enlarge the range of the verifi- cation. Besides, this method can be tested through application in the subsequent measurement of volume and early diagnosis of cartilage lesions.

Acknowledgment: This research is funded by National Natural Science Foundation of China NSFC (No: 61108086, 61171089, 11304382), Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Min- istry, Fundamental Research Funds for the Central Universi- ties (CDJZR12160011, CDJZR13160008, CDJZR155507), the China Postdoctoral Science Foundation (2013M532153), the Chongqing Postdoctoral Science Special Foundation of China and 2015 Chongqing University Postgraduates’ Innovation Project.

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Received: xx Xxxx xxxx. Accepted: xx Xxxx xxxx.

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