Magnetic Resonance Imaging Image Segmentation under Edge Detection Intelligent Algorithm in Diagnosis of Surgical Wrist Joint Injuries

Abstract

Background. Wrist joint injury refers to the injury of the wrist joint caused by excessive stretching of the ligaments and joint capsules around the joint caused by indirect violence. The tissue structure of the wrist joint is complex, and the clinical diagnosis effect is poor. Methods. The purpose of this study was to improve the diagnostic accuracy of wrist joint injuries and provide evidence for imaging analysis and automatic diagnosis of lesions in patients with wrist joint injuries. The Canny algorithm was adopted to extract the edge features of the patient’s magnetic resonance imaging (MRI) image, and the particle swarm optimization-support vector machine (PSO-SVM) algorithm was applied to segment the lesion. The image processing effect of the algorithm was evaluated by taking peak signal to noise ratio (PSNR), mean square error (MSE), figure of merit (FOM), and structural similarity (SSIM) as indicators. The accuracy, sensitivity, specificity, and Dice similarity coefficient of the algorithm were analyzed to evaluate the diagnostic accuracy in WJI. Results. Compared with the Gradient Vector Flo (GVF) algorithm and the Elastic Automatic Region Growing (ERG) algorithm, the edge stability of the PSO-SVM algorithm was stable above 0.9. After the quality of images processed using different algorithms was analyzed, it was found that the PSNR of the PSO-SVM algorithm was 26.891 ± 5.331 dB, the MSE was 0.0014 ± 0.0003, the FOM was 0.8832 ± 0.0957, and the SSIM was 0.9032 ± 0.0807. The four indicators were all much better than those of the GVF algorithm and the EARG algorithm, showing statistically obvious differences ( P < 0.05). Analysis on diagnostic accuracy of different algorithms for WJI suggested that the diagnostic accuracy of the PSO-SVM algorithm was 0.9413, the sensitivity was 0.9129, the specificity was 0.9088, and the Dice similarity coefficient was 0.8715. The four indicators all showed statistically great difference compared with those of the GVF algorithm and the EARG algorithm ( P < 0.05). Conclusions. The PSO-SVM algorithm showed excellent edge detection performance and higher accuracy in the diagnosis of WJI, which can assist clinicians in the clinical auxiliary diagnosis of WJI.

Artificial intelligence powered advancements in upper extremity joint MRI: A review

“…Due to the complex wrist structure, it is challenging to achieve automated segmentation because of the artifacts and uneven grayscale produced by MRI scanning [ 67 ]. Therefore, Li et al [ 68 ] first used the Canny operator to extract the edge of images from patients with wrist injuries, then classified the extracted edges by particle swarm optimization-support vector machine (PSO-SVM) algorithm, achieving a sound effect of edge detection, thus diagnosed wrist joint injury (WJI) with the diagnostic accuracy of 0.9413.…”

Image edge detection using pseudo-Boolean polynomials

“…An image edge detection can be achieved by separating discontinuities in image intensity, colour, or texture [13,14]. Over the years numerous edge detection techniques have been proposed ranging from classical gradient-based methods [2,3] to more recent approaches based on machine learning [15,16] and deep learning [17,18]. Image edge detection is mostly a processing step in myriads of computer vision and image processing tasks such as image segmentation, object recognition and image compression [19].…”