Background Acromial anatomy has been found to be correlated with degenerative full-thickness rotator cuff tears in current studies. However, research on the relationship between acromial anatomy and articular-sided partial thickness of rotator cuff tears (PTRCTs) is still lacking. The purpose of this study was to evaluate whether these imaging graphic parameters exhibit any association between acromial anatomy and degenerative articular-sided PTRCTs. Methods Between January 2016 and December 2018, a total of 91 patients without a history of trauma underwent arthroscopy as an articular-sided PTRCT group. In the control group, with age- and sex-matched patients, we selected 91 consecutive outpatient patients who underwent shoulder magnetic resonance imaging (MRI) because of shoulder pain and an MRI diagnosis of only synovial hyperplasia and effusion. MRI was used to measure the acromial type, acromiohumeral distance (AHD), lateral acromial angle (LAA), acromion index (AI), and critical shoulder angle (CSA) by 2 independent observers. Results The acromion type, AHD and LAA showed no difference between degenerative articular-sided PTRCTs and controls (P = 0.532, 0.277, and 0.108, respectively). AI and CSA were significantly higher in degenerative articular-sided PTRCTs (P = 0.002 and 0.003, respectively). A good correlation was found between AI and CSA to measurement(Pearson correlation coefficient = 0.631). Conclusions Our study revealed that higher AI and CSA were found in degenerative articular-sided PTRCTs. Acromial anatomy with a large acromial extension was associated with the occurrence of degenerative articular-sided PTRCTs.
In this paper, first we introduce the digital image algorithm of Canny edge detection, and then simulation results of Canny edge detection are given particularly. Test results show that the improved algorithm improves the accuracy of edge detection effectively. Secondly, the algorithm of the region growth, which algorithm is divided into two parts: one is growing region; the other is merging region, is realized. Finally, based on above analysis, we find out that a continuous boundary is not obtained by edge detection, and the method of regional extraction may produce transition segmentation. In response to these deficiencies, we present a region growth algorithm of edge detection.
Recent years, the image sparse representation has been the popular method in the study of image representation, which has put forward a new idea in the image denoising. Its basic principle is that the original image has the sparse representation under the proper over-complete dictionary. Filter out the noise, we should find out the sparse representation of the image through the design of the dictionary. Its mechanism is that one hand the useful information of the image would be effectively expressed because of the sparse decomposition algorithm based on the redundant dictionary. The other the noise would not be expressed through the dictionary atoms. We do the image denoising according to the image sparse representation. Because of the superiority of the adaptive dictionary algorithm in the image, in this paper, we discuss the over-complete dictionary training algorithm. And we prove the effectiveness through the MATLAB.
Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm for image segmentation, and has been widely applied because the segmentation results are consistent with human visual characteristics. Enhanced fuzzy c-means clustering (EnFCM) algorithm is the improved FCM algorithm, which reduces the computational complexity. But, both FCM algorithm and EnFCM algorithm, clustering number still need to be manually determined. This paper, in order to realize the automation degree of algorithm, presents an improved algorithm. It first analyzes the histogram, then automatically determines the clustering number and peak value of each class through use of the peak point detection technology, finally segments image by using EnFCM algorithm. Experiments show that this method is a kind of faster fuzzy clustering algorithm with automatic classification ability for image segmentation.
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