The position of the hinge point of mitral annulus (MA) is important for segmentation, modeling and multimodalities registration of cardiac structures. The main difficulties in identifying the hinge point of MA are the inherent noisy, low resolution of echocardiography, and so on. This work aims to automatically detect the hinge point of MA by combining local context feature with additive support vector machines (SVM) classifier. The innovations are as follows: (1) designing a local context feature for MA in cardiac ultrasound image; (2) applying the additive kernel SVM classifier to identify the candidates of the hinge point of MA; (3) designing a weighted density field of candidates which represents the blocks of candidates; and (4) estimating an adaptive threshold on the weighted density field to get the position of the hinge point of MA and exclude the error from SVM classifier. The proposed algorithm is tested on echocardiographic four-chamber image sequence of 10 pediatric patients. Compared with the manual selected hinge points of MA which are selected by professional doctors, the mean error is in 0.96 ± 1.04 mm. Additive SVM classifier can fast and accurately identify the MA hinge point.
Congenital heart disease is one of the main reasons for death in children. The 3D shape and movement of the mitral apparatus are significant to analyze the function of left ventricular, diagnose mitral valve disease, and identify disorder of left ventricular [
Precise positioning the hinge point of mitral value is helpful for modeling, motion tracking, and multimodalities registration of cardiac images. The vague and incomplete ventricular structure in ultrasound images due to the heavy noise, low resolution, and limited imaging range in real time echocardiography causes great difficulties to identify the mitral value manually and automatically. Nevo et al. [
This paper introduces a hinge point of mitral annulus identification algorithm based on additive SVM classifier [
SVM and boosted decision tree [
The linear SVM is more efficient, but many nonlinear kernels can get better results in pattern classification tasks due to the nonlinear distribution of features. Some popular nonlinear kernels which are based on histograms of low-level features like color and texture of the image use a kernel derived from histogram intersection or chi-squared distance to train a SVM classifier. To evaluate the classification function, the test histogram is compared with every support vector histogram. Maji et al. [
Given training set
With similarity of feature such as boundary, color can be represented as histogram which regularly uses histogram intersection as its evaluation of similarity. The histogram intersection kernel is
So as to make the complexity of
The general image detection operator such as the Sobel operator and the Laplace operator cannot be applied to ultrasound images due to the heavy noise and blurred boundary. Context is the relationship with the neighbors which can be represented as a certain range of neighbors of a pixel in image processing. This paper introduces a local context feature which sparsely sample [
As Figure
Local context feature.
We can get a good recognition result by adopting the local context feature and additive kernel SVM classifier, as Figure
Classification result of additive SVM.
Result of SVM classifier
Weighted density filed
Result of adaptive threshold
The SVM classifier is trained to get points like that arrow 1 indicates, so the majority of candidate points will be right. And density is a good feature to distinguish the right points and wrong ones. This paper applies a weighted template to each candidate point and gets a weighted density field. Figure
The weighted template obtained by block distance.
The density field function is
If the number of continuous areas greater than If the number of areas is less than two, decrease If the number of areas is greater than two, increase Get the adaptive threshold
Figure
Refined classification result of
Result of additive kernel SVM
Refined result using
The flow of classification procedure can be integrated into three layers shown in Figure
Flow of classification.
The image data in the paper is from Sonos 7500 ultrasound image and the size of raw 3D image is 208 × 160 × 144. Data is acquired from 10 children who are from 9 to 12 years old. The cardiac cycle has 9 to 24 frames. Experiment 1 shows how to confirm the sampling window of local context feature and how to choose the size of weighted template. Experiment 2 compares the 3
Local context feature is to sample local structure of heart tissue. The specific size of sampling window and the size of weighted template are from experiments. And the experiment results show that different size has little influence to the results. Figure
Identification results by different parameters.
Identification result
Identification result
Identification result
Identification result
Figure
The classification result of different sample mode.
Result of direct sampling
Result of average sampling
Table
Errors between our segmentation method and manual segmentation results.
Septal | Lateral | |||||||
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Mean | Variance | Mean | Variance | Mean | Variance | Mean | Variance | |
mm | 0.96 | 0.907 | 1.12 | 0.69 | 1.34 | 1.39 | 0.75 | 0.48 |
Figure
The classification result of different kernel function.
Classification result using intersection kernel function
Classification result using chi-square kernel function
Classification result using the Jensen-Shannon kernel function
This paper introduced a hinge point of mitral annulus identification method using additive kernel SVM classifier and local context feature. Due to the classification errors, we design a weighted template to exclude the obvious wrong points. After refining the result, the mean and variance of error between automatic and manual result are controlled in 0.96 ± 1.04 mm. From the experiments, it is demonstrated that this algorithm can accurately locate the hinge point of mitral valve. For the fast feature extraction and accelerated classification procedure, this algorithm can be used in real-time applications.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The work in this paper was partly funded by the Scientific Research Fund of Hunan Provincial Education Department (Grant No. 12B003), the Scientific Research Fund of Hunan Provincial Transportation Department (Grant No. 201334).