| Medical image processing plays an important role on the doctor’s diagnosis and treatment. Examination of many diseases in the hospital relay on the shooting CT images, including examination of the lung disease, lung cancer incidence rate grow by0.5%per year, is malignant tumors with the highest incidence and mortality rates in the world, the treatment of these diseases have to rely on the shooting CT image. If doctors observe a large number of CT images for a long time, it is inevitable to produce visual fatigue, and it is difficult to observe some small lesions with the naked eye, so it becomes extremely important to process medical lung CT image through computer. The image edge detection is premise of image segmentation, fusion and three-dimensional reconstruction, also is an important part of the image processing to help improve the efficiency of the doctor’s diagnosis. As for previous common image edge detection method, detection accuracy is not high, anti-noise performance is poor and other aspects shortcoming, for these problem, The paper realized the classic edge detection algorithm, and then proposed two improved edge detection algorithm after studying the wavelet transform and mathematical morphological edge detection algorithm, at last, implemented the two improved algorithms on the DSP hardware through the CCS software programming.For the research of image edge detection algorithm based on wavelet transform and mathematical morphology, this paper do some work, which is as follows: (1)Study several common image edge detection algorithms appeared in recent years, such as the classic edge detection algorithm, edge detection algorithm based on wavelet transform and edge detection algorithm based on mathematical morphology, and simulate these algorithms respectively in MATLAB.(2)Under the premise of studying image edge detection algorithm based on wavelet transform and mathematical morphology, the paper proposed two improved algorithms, one is the wavelet thresholding joint improved morphological edge detection algorithm; another is improved separately mathematical morphology edge detection algorithm; and it simulated the two improved algorithms in MATLAB, experimental results show that the improved algorithms are able to detect a higher resolution and better anti-noise performance edge, at the same time, verify the effectiveness and feasibility of the improved algorithm.(3) Implement the improved edge detection algorithms in the DSP hardware. Using video graphics hardware development platform which the core is TMS320DM6446microprocessor as the hardware systems. Programming image edge detection algorithms through the corresponding software implementation environment CCS (Code Composer Studio), display the experimental results by the CCS graphics display window to verify the implemented results of algorithms in hardware and the feasibility of algorithms. |
