Joint is an indirect connection form among human bones and is an important part of the motion system [
Wrist joint injury (WJI) includes ligament, synovial damage, articular cartilage, bone cortical damage, and bone marrow lesions caused by trauma [
Traditional WJI diagnosis usually requires clinicians to analyze and judge one by one based on the patient’s MRI images, but this diagnosis method is subjective, and the judgment result will inevitably affect clinical diagnosis and treatment to a certain extent [
In this study, 160 WJI patients who admitted in hospital from August 2018 to April 2021 were selected as the research subjects, including 73 males and 87 females, aged 21–68 years old (with an average age of 45.1 ± 18.8 years old). This study had been approved by the ethics committee of hospital. All patients and their families were aware of the study and had signed the informed consent forms.
The inclusion criteria were defined as follows: patients ≥18 years old, and patients had a history of wrist injury within 1 week.
The exclusion criteria were defined as follows: patient with cognitive impairment; patients had contraindications to MRI scanning; and patients had wrist joint infection, tumor, or pathological fracture caused by other diseases.
All patients underwent MRI scans in the wrist joints using superconducting MRI machines. The method was as follows: the scanning slice thickness was 3 mm, and the slice spacing was 2 mm. The sagittal and coronal spin echo sequence (T1WI) and the transverse short-time reversal sequence (STIR) were used to scan the patients during the scanning process. Among them, T1WI sequence (time of repetition (TR) and time of echo (TE) were 545 and 120, respectively; ETL5, field of view (FOV) was 14 × 14 cm, and matrix was 512 × 512); STIR sequence (TR and TE were 3400 and 30, respectively; ETL12, FOV was 10 × 10 cm, and the matrix was 576 × 576); and coronal STIR sequence (TR was 3000, TE was 30, ETL12, FOV was 10 × 10 cm, and matrix was 512 × 512). After the scan, comprehensive analysis of the patient’s MRI image is performed to obtain the diagnosis result.
When a patient’s MRI image was segmented, the amplitude of the radio frequency signal in MRI represented the image intensity of each image. On an image, there was a unique measurement image at each position, which was called a scalar image. The measured image with more than one images was called a vector image. MRI was to obtain images in a discrete space, and image segmentation was to divide an image into regions that were not overlapped with each other.
In the above equation,
In equations (
A two-dimensional Gaussian function was adopted to smooth the original image, and the obtained smoothed image function expression was as follows:
In the above equation,
In the above equation,
On the basis of obtaining the partial derivatives in the
In equations (
In the equation above,
In the particle swarm
Similarly, the velocity of each particle corresponded to a
In the PSO algorithm, each particle had to be performed with the iterative optimization to approach the position of the global optimal solution. In the optimization process, the historical best position and the best position of the group that each particle passes can be expressed by the following equation:
The iterative equation of velocity and position of particle
Here,
The optimization process of PSO algorithm is shown in Figure
Optimization of PSO algorithm.
The PSO algorithm showed small data volume, simple operation, and high computational efficiency, but it was easy to fall into the local optimal solution in the later stage of optimization. Therefore, the SVM algorithm was adopted to obtain the global optimal solution under limited information. It was assumed that there was a data set
In equation (
The parameters
The constraints were defined as follows:
The Lagrangian function was introduced to solve the constrained optimization of the SVM algorithm:
In the above equation,
The constraints were defined as follows:
According to the above equation, the optimal solution about the Lagrangian coefficient was obtained:
The optimal weight and the optimal offset were expressed as the following equations:
In equations (
When the optimal decision function was calculated, only the support vector had to be summed, and the support vector at
In equations (
Classifications of linearly separable SVM.
Overall flow chart of this thesis.
For the MRI image segmentation effect of all WJI patients participating in this study, subjective evaluation and objective evaluation were used. The main indicators of subjective evaluation were peak signal to noise ratio (PSNR), mean square error (MSE), figure of merit (FOM), and structural similarity (SSIM). The functional expressions of the above indicators were as follows:
In equations (
Edge continuity and edge credibility are two important evaluation indicators. Whether the edge is continuous is directly related to whether the extraction of the target area is complete. The calculation equation can be expressed as follows:
In the above equation, the value range of
The calculation equation of the marginal credibility index can be expressed as follows:
In the above equation,
The manual segmentation results of clinically experienced orthopedic surgeons were undertaken as the gold standard to analyze the accuracy, sensitivity, specificity, and Dice similarity coefficient of PSO-SVM algorithm in diagnosis of WJI. The calculation equation was as follows:
In equations (
In this study, SPSS 22.0 software was used for data processing. Measurement data were expressed as mean ± standard deviation, and measurement data were expressed as %. The comparison between groups was performed by SNK-
In this study, the Canny algorithm was adopted to extract the edge points of the image, and the ratio coefficient (th/tl) of its high and low thresholds determined the edge detection effect of the algorithm. Therefore, th/tl was modified, and th/tl was set to = (0.3, 0.6, 0.9, 1.2, 1.5, 1.8) to observe the image edge detection results under different iteration scale factors. The detection results were shown in Figure
The difference between edge continuity and edge credibility of different scale coefficients. (a) Edge continuity. (b) Edge credibility.
After the Canny algorithm scale factor was adjusted to 1.8, edge features were extracted from the WJI patient’s MRI image. The results (Figure
The edge extraction results of Canny algorithm.
The algorithm training was performed on the MRI images of patients participating in this study, and the edge extraction results were shown in Figure
Schematic diagram of the segmentation results of the PSO-SVM algorithm.
The mean ± standard deviation was adopted to evaluate the overall level of the image segmentation results, and the GVF and EARG algorithms were introduced for comparison, so as to avoid the contingency of the edge detection results of the PSO-SVM algorithm. The results shown in Figure
The difference between edge continuity and edge credibility of different algorithms. (a) Edge continuity. (b) Edge credibility.
The PSNR, MSE, FOM, and SSIM were adopted to quantitatively objectively evaluate the processing effect of the PSO-SVM algorithm on the MRI image. The results given in Figure
The objective evaluation of different algorithms on the effect of MRI image processing in patients with WJI.
The pathological diagnosis showed that there were 48 cases of occult fractures, 67 cases of displaced fractures, and 45 cases of dislocations. The accuracy, sensitivity, specificity, and Dice similarity coefficient of the three groups were compared, and the results were shown in Figure
Diagnosis results of different algorithms.
The wrist joint is formed by intertwining ligaments and attaching each carpal bone. It is frequently used in daily life, so it is easily damaged. If the treatment is improper, it is very easy to cause delayed WJI healing, avascular necrosis of the wrist, joint instability, and even deformity of the wrist joint in severe cases [
In this study, the Canny algorithm was used to extract the edge of the image, and the edge detection effect of the Canny algorithm was detected through the scale coefficients of high and low thresholds. The results indicated that when the scale factor was 1.8, the Canny algorithm showed the highest edge continuity and edge credibility. Under the high threshold, the Canny algorithm showed serious edge loss during extraction, while under the low threshold, there were too many detected edges. After the scale factor was adjusted to 1.8, the Canny algorithm showed the best edge extraction effect, but it was still difficult to extract the lesion area in the image. Regarding the threshold of the Canny edge detection algorithm, Parthasarathy et al.’s research on MRI images of brain tumors also reached similar conclusions [
In this study, the Canny algorithm was used to extract the edge features of the WJI patient’s MRI image and then the PSO-SVM algorithm was used to classify the extracted edges, achieving an excellent edge detection effect. The PSO-SVM algorithm showed higher edge continuity and credibility, better segmentation performance, and higher diagnostic accuracy for WJI. This algorithm not only greatly improved the efficiency of segmentation, reduced a large number of manual repetitive operations, and can assist clinicians in the clinical auxiliary diagnosis of WJI, so it showed high theoretical and practical significance. However, there were still some shortcomings in this study. The structure of the wrist joint was complex, which resulted in large differences in the positions and features of various parts of the MRI images of different patients. Therefore, the running time of the algorithm was long. In addition, it failed to analyze the auxiliary diagnosis effect of the algorithm on WJI. Such shortcomings had to be improved in the follow-up work.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare no conflicts of interest.