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

Abstract

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.

Study on Object-Based High-Resolution Remote Sensing Image Classification of Crop Planting Structure in the Loess Plateau of Eastern Gansu Province

“…Cannyedge detection is able to obtain more accurate edge information and filter noise with less time. [36,37]. In addition to combining multiple feature learning,, Object-Based Image Classification also increases the dimension of feature space and reduces the efficiency of data processing, of which secondary features may result in noise and even lower the classification accuracy.…”

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

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”