In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images, a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper. In this method, first, original 3D human brain image information is collected, and CT image filtering is performed to the collected information through the gradient value decomposition method, and edge contour features of the 3D human brain CT image are extracted. Then, the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points, and the 3D human brain CT image is reconstructed with the salient feature point as center. Simulation results show that the method proposed in this paper can provide accuracy up to 100% when the signal-to-noise ratio is 0, and with the increase of signal-to-noise ratio, the accuracy provided by this method is stable at 100%. Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is significantly better than traditional methods in pathological feature estimation accuracy, and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

In order to improve the diagnosis and analysis ability of 3D spiral CT and to reconstruct the contour of 3D spiral CT damage image, a contour reconstruction method based on sharpening template enhancement for 3D spiral CT damage image is proposed. This method uses the active contour LasSO model to extract the contour feature of the 3D spiral CT damage image and enhances the information by sharpening the template enhancement technique and makes the noise separation of the 3D spiral CT damage image. The spiral CT image was processed with ENT, and the statistical shape model of 3D spiral CT damage image was established. The gradient algorithm is used to decompose the feature to realize the analysis and reconstruction of the contour feature of the 3D spiral CT damage image, so as to improve the adaptive feature matching ability and the ability to locate the abnormal feature points. The simulation results show that in the 3D spiral CT damage image contour reconstruction, the proposed method performs well in the feature matching of the output pixels, shortens the contour reconstruction time by 20/ms, and provides a strong ability to express the image information. The normalized reconstruction error of CES is 30%, which improves the recognition ability of 3D spiral CT damage image, and increases the signal-to-noise ratio of peak output by 40 dB over other methods.

针对布匹瑕疵种类多以及单个方法仅对特定类瑕疵有效的问题, 提出一种新的基于小波多尺度积和数学形态学的布匹瑕疵检测算法。首先对输入图像进行二进小波变换后, 低频近似子图经过数学形态学运算, 得到良好的瑕疵形状特征, 然后对高频子图使用小波多尺度积方法, 可以抑制噪声的同时增强瑕疵的边缘线性特征, 最后融合得到最终的检测结果。实验结果表明, 该算法在虚警率和运算时间较低的同时, 得到较高的检测率, 综合性能优于经典的 Gabor和小波变换算法。