Compressed sensing based CT reconstruction algorithm combined with modified Canny edge detection

Hsieh, Chia Jui; Huang, Ta Ko; Hsieh, Tung-Han; Chen, Guo Huei; Shih, Kun Long; Chen, Zhan Yu; Chen, Jyh Cheng; Chu, Woei Chyn

doi:10.1088/1361-6560/aacece

Cited by 24 publications

(15 citation statements)

References 41 publications

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“…e above results all showed the advantages of the improved Canny algorithm for CT image processing. Hsieh et al [13] pointed out that the improved Canny operator can effectively detect the edge position of the object in the low-dose reconstruction process so that the image quality of CT reconstruction was better than other algorithms. Such results were consistent with the results of this study.…”

Section: Discussionmentioning

confidence: 99%

“…e accuracy of medical image edge detection directly determines the accuracy of feature extraction, which in turn affects the subsequent work [12]. However, noise is often found in the process of image acquisition and storage, resulting in blurred images or imperfections that lead to inaccurate readings [13]. At this time, the image has to be smoothed.…”

Section: Introductionmentioning

confidence: 99%

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Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction

Zhang

et al. 2021

Scientific Programming

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This study was to explore the effect of computed tomography (CT) images processed by image edge detection technology based on the improved Canny algorithm in the diagnosis of stroke patients with mobility dysfunction and to evaluate the clinical application value of early rehabilitation nursing (ERN). 114 patients who were diagnosed and treated in hospital and were suspected of having stroke movement dysfunction were selected as the research objects, and they were randomly divided into two groups, each with 57 patients. Patients in the control group were diagnosed with conventional CT examination, and the patients in observation group were diagnosed based on the CT images processed by the image edge detection technology based on the improved Canny algorithm. Patients in the observation group were divided into a group C and a group O. Patients (27 cases) in group O received rehabilitation training within 3 days after their vital signs were stabilized, and patients (30 cases) in group C received rehabilitation training within 3∼7 days after their condition was stabilized. The CT image diagnosis effects on patients of the control group and the observation group were analyzed, and the ERN effect on patients of the C group and the O group was compared. The results showed that the mean square error (MSE) of the improved Canny algorithm (233.78) was smaller than that of the traditional Canny algorithm and Sobel and Prewitt algorithm, and the peak signal-to-noise ratio (PSNR) (27.89) was greater than that of the traditional Canny algorithm and Sobel and Prewitt algorithm ( P < 0.05 ). Compared with the control group, the sensitivity (85.00% vs. 62.12%), specificity (70.59% vs. 36.36%), and accuracy (80.70% vs. 54.39%) of the examination method of the observation group were much higher ( P < 0.05 ). In addition, the total effective rate of patients in group O was 89.47%, which was greatly higher than that of group C (70.18%), and the scores of Meyer index and Barthel index were also higher in contrast to those of group C ( P < 0.05 ). In conclusion, the improved Canny algorithm showed a clearer display on the edge detection of CT images and good application effect. It showed the effect of making conventional CT more accurate in the examination and diagnosis of stroke patients, and it was worthy of clinical application and promotion. The research showed that the timelier rehabilitation training, the better the treatment effect of patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction

Zhang

et al. 2021

Scientific Programming

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“…SSIM is employed to measure the structural similarity between graphs, and the larger the value, the more similar the reconstructed image to the original [15]. PSNR is applied to evaluate the image quality, and the larger the value, the lower the distortion degree of reconstructed image [8]. is disclosed that the algorithm proposed in this study was adopted to reconstruct the patient's original CT image and would not lose useful information of the original image, and the definition was higher.…”

Section: Discussionmentioning

confidence: 99%

“…e use of intelligent algorithms for medical image processing can improve the efficiency of clinical diagnosis. Hsieh et al modified the Canny operator and CS algorithm to improve the edge blur during CT reconstruction [8]. Rajendran et al built a spectral prior image constrained compressed sensing framework based on the modified CS algorithm and applied it to lung CT images to visualize lung nodules [9].…”

Section: Introductionmentioning

confidence: 99%

Optimized Reconstruction Algorithm Based Analysis of Effects of Exercise Therapy Combined with Celecoxib Capsules on the Rehabilitation of Knee Osteoarthritis Using Computed Tomography Images

Ying

Yang

Wang

2021

Scientific Programming

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The aim was to explore the application value of computed tomography (CT) images based on optimized reconstruction algorithm (ORA) in knee osteoarthritis (KOA) and to analyze the rehabilitation effect of exercise therapy combined with celecoxib capsules on KOA. On the basis of iterative reconstruction algorithm, sparse data and compressed sensing (CS) algorithm were introduced in this study for the image reconstruction and were compared with the reconstruction effects of other algorithms. 105 patients with KOA were selected as the subjects investigated, who were treated in the hospital from December 2018 to January 2020. They were divided randomly into group A (exercise therapy), group B (celecoxib capsule therapy), and group C (exercise therapy combined with celecoxib capsules), with 35 patients in each group. All the patients were scanned by CT. Visual analogue score (VAS) method was adopted to compare the pain symptoms of patients in the three groups before and after treatment. The Lysholm knee function score was employed to evaluate the knee function of the patient before and after treatment, and finally the difference in the total effective rate of treatment was compared. The results showed that the reconstruction algorithm in this study had a higher degree of coincidence with the original image, and its root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were greatly higher than those of filtered back-projection (FBP) algorithm. There was no huge difference between Lysholm score and VAS in patients of the three groups before treatment ( P > 0.05 ). Besides, Lysholm score in all dimensions and its total score after treatment were higher than those of patients in the three groups before treatment ( P < 0.05 ), and those of patients in group C increased extremely in contrast to group A ( P < 0.05 ). VAS after treatment were all lower in patients of the three groups than those before treatment ( P < 0.05 ), and VAS of patients in group C were sharply lower than those of group A ( P < 0.05 ), but the total effective rate of patients in group C was dramatically higher than the rate of group A ( P < 0.05 ). It indicated that an algorithm with short scanning time and good image quality was established based on iterative reconstruction algorithm. Exercise therapy combined with celecoxib capsules could alleviate the pain symptoms and improve the function of knee joint, which was more advantageous than the simple rehabilitation comprehensive therapy. Therefore, it had higher clinical value for the patients with KOA.

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“…The edge detection algorithm was a partial image segmentation method. Its purpose was to use the feature of the extreme difference between the grayscale of the edge point of the image and the adjacent grayscale, and the image edge can be obtained by solving maximum values of the first-order and second-order degrees of the image in the horizontal and vertical directions [ 20 ]. When edge detection was performed, it had to use the edge detection operator to detect all possible edge points in the image.…”

Section: Methodsmentioning

confidence: 99%

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

2021

Contrast Media & Molecular Imaging

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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.

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Compressed sensing based CT reconstruction algorithm combined with modified Canny edge detection

Cited by 24 publications

References 41 publications

Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction

Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction

Optimized Reconstruction Algorithm Based Analysis of Effects of Exercise Therapy Combined with Celecoxib Capsules on the Rehabilitation of Knee Osteoarthritis Using Computed Tomography Images

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

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