Magnetic Resonance Imaging Based on Wavelet Algorithm in the Diagnosis and Treatment of Tibial Osteomyelitis Wound Infection

This study was to explore the therapeutic effect of magnetic resonance imaging (MRI) images based on the image processing algorithm under the correlation of dyadic wavelet coefficients on the diagnosis of tibial osteomyelitis patients. 32 tibial osteomyelitis patients admitted to hospital were randomly selected as the research objects. According to the patients’ wishes, patients who were willing to use new MRI imaging techniques for disease detection were set as the experimental group and conventional MRI imaging detection methods were set as the control group. The application effect of the new MRI imaging technology was evaluated by comparing the treatment effect of the two groups of patients. It was found that the mean square error (MSE) (38.5642) and signal-to-noise ratio (SNR) (18.5122) processed by the improved wavelet algorithm were much better than those of unimproved dyadic wavelet algorithm (59.1096 and 15.2341) ( P < 0.05 ). The possibilities of soft tissue swelling, bone invasion or destruction, thickening and sclerosis of bone cortex, bone abscess, periosteum response, dense dead bone, and bone sinus of patients in the experimental group were higher than those of the control group, which were 100% vs. 55%, 100% vs. 80%, 92% vs. 65%, 50% vs. 25%, 42% vs. 15%, 67% vs. 45%, and 50% vs. 15%, respectively ( P < 0.05 ). The healing time of osteomyelitis (22.89 ± 2.19 d vs. 32.32 ± 2.81 d) and the recovery of wound infection (14% vs. 45%) in the patients in control and experimental groups showed that the results of the experimental group were obviously better than those of the control group. The kappa value of the diagnosis results and tissue biopsy of the experimental group was higher than that of the control group (0.45 vs. 0.34) ( P < 0.05 ). In conclusion, the results of the enhanced and improved MRI images were relatively more accurate and the treatment methods adopted were more symptomatic, resulting in more effective treatment. In addition, the wavelet algorithm had certain application value in the enhancement processing of medical images and showed a good development prospect.

Batch similarity comparison of line traces based on multi-strategy machine learning

Conventional methods can scarcely achieve the rapid comparison of line traces. In this study, trace detection signals based on a single-point laser were collected and smoothed. The multi-scale wavelet coefficient of the trace detection signals was obtained after noise reduction by the dual-tree complex wavelet algorithm to minimize the adverse effects of data size on successive comparison calculations. Batch similarity comparison of trace features was achieved using multiple comparison strategies, including an optimized dynamic time regulation algorithm and the recognition of the changing rate gradient. Based on the results of boosting fusion multi-strategy comparison, optimized comparisons were achieved by machine learning, and a model for the rapid comparison of trace features was established. Finally, the viability of the proposed algorithm was verified by experiments.

Wavelet method of edge detection in images with high-noise level

In the paper a mathematical model addressed to non-sharp edges in the images is proposed. This model is based on and integral transform with Haar-Gauss wavelet and matching algorithm of bandwidth, such model is used to detection of the edges in images with high-level noises, both in the x plane and the frequency domains. There is shown that applying the integral Haar-Gaussian transformation the detection of single and double edges is possible. Demonstrated in the paper results confirm that wavelet transform supported by the matching wavelet algorithm of wavelength bandwidth make an important exploration tool of the images with the edges possessing a large depth of sharpness.