Recently, diagnosis, therapy and monitoring of human diseases involve a variety of imaging modalities , such as magnetic resonance imaging(MRI),computed tomography(CT),Ultrasound(US) and Positron-emission tomography(PET)as well as a variety of modern optical techniques .The degeneration of lumbar intervertebral disc has become a common disease in modern society. Currently, the most commonly used method is the diagnostic grade based on MRI technology, among which Pfirrmann grading system is most widely used in clinic. The Pfirrmann grading system is mainly based on the measurement of the average height of the lumbar intervertebral disc and the intensity of the signal of the nucleus pulposus and the inner and outer edge of the fiber ring in MR images. With the degeneration of the intervertebral disc, the signal of the inner and outer edge of the annulus also decreases, so the error caused by the method of measuring the average height of the lumbar intervertebral disc is larger. Therefore, we proposed an algorithm based on morphology to detect lumbar intervertebral disc in MRI spinal images. First, the median filter is used to remove noise in MRI and then the lumbar intervertebral disc is extracted through morphological processing. Then, the image is smoothed by combining with gaussian filtering. Finally, the result map of lumbar intervertebral disc is obtained and its area is calculated. In the analysis and comparison of the detection results of the lumbar intervertebral disc, the skeleton extraction diagram of the detection results of the lumbar intervertebral disc was obtained after processing the image of the detection results of the lumbar intervertebral disc with the thinning algorithm. According to the analysis, the degree of laminar disc skeleton and upper and lower vertebral body is as high as 90%. This paper also briefly introduces the application direction of this measurement algorithm in medicine: 1. Improve doctors' ability to detect early lumbar disc degeneration.2. Assist doctors to observe postoperative recovery of patients.
KEYWORDS: RGB color model, Digital watermarking, Data modeling, Performance modeling, Neural networks, Intellectual property, Mathematics, Computer science, Process modeling, Visual process modeling
Amid the maturity of machine learning, deep neural networks are gradually applied in the business sector rather than be restricted in the laboratory. However, its intellectual property protection encounters a significant challenge. In this paper, we aim at embedding a unique identity number (ID) to the deep neural network for model ownership verification. To this end, a scheme of generating DNN ID is proposed, which is the criterion for model ownership verification. After embedding, the model can complete the original performance and own a unique ID of this model as well. DNN ID can only be generated by the owner to check the model authorship. We evaluate this method on MNIST. Experiment results demonstrate that the DNN ID can accurately verify the ownership of our trained model.
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