Improved tooth flank region segmentation method of interference image by U-net neural network

Laser interferometry is an effective method to measure the tooth flank topography errors, and the information of tooth flank topography can be obtained accurately by using interferometric image processing technology. However, in practical experiments, speckle noise and tooth flank stray light often lead to the reduction of segmentation accuracy, thus affecting the accuracy of measurement. At the same time, the traditional time-domain segmentation method has the problems such as fitting error and being difficult to determine the appropriate segmentation threshold, which will also affect the segmentation accuracy. Therefore, tooth region segmentation plays an important role in interference image processing, which can effectively improve the measurement accuracy. To solve this problem, this paper presents a tooth flank region segmentation method based on improved U-net neural network. Firstly, the SE attention mechanism was introduced into the traditional U-net architecture to improve the recognition accuracy of the tooth flank region. Secondly, the mapping relationship between the input and output of the network was established to enhance the generalization ability of the network by collecting the tooth flank object images and interference images under different lighting environments and incident angles as the training data set. Finally, an experimental comparison was conducted between the methods presented in the article and traditional segmentation methods. The results show that the proposed method can effectively reduce the segmentation error introduced by the traditional method, the phase jump correction rate is about 75.6%, and the accuracy of the model is 96%, which improves the segmentation accuracy and measurement accuracy. This research has wide application potential in the fields of interference image, medical and remote sensing image processing.