A Variation Method For Image Segmentation Based On Wavelet Transform

In recent years,with the wide application of digital images in medicine,traffic monitoring,etc.,image analysis has become a research hotspot.Image segmentation is one of the most fundamental and important task in image analysis,it aims to divide an image into several coessential regions,so as to extract the interested object in the image.Image segmentation is the precondition of most image analysis and image understanding tasks,it is of great importance to explore effective image segmentation methods.Experienced decades of development,various theories and methods have been applied to image segmentation method,among them,active contour method has been paid more and more attention in recent years.Compared with other segmentation method,active contour method can provide continuous with sub-pixel precision segmentation results.In addition,the application of level set theory,makes the realization of active contour method more flexibility and convenience.Therefore,this paper mainly proposes an improved image segmentation method based on the existing level set image segmentation methods.The main research contents are as follows:(1)When the existing classical LBF model extracts the edge of the image,the effect is unsatisfactory and the segmentation efficiency is low for images with intensity inhomogeneity.Therefore,the edge information of the image is first extracted using the wavelet transform feature,and then edge detection function is constructed on the basis of this,which is introduced into the LBF model to obtain a new LBF model(WLBF model)based on the wavelet transform.Finally,the test image is selected and the segmentation effect of the method is compared with the existing model.The experimental results show that the model is accurate and effective.(2)For the existing C-V model,taking into account the difficulty of segmentation of grayscale non-uniform images and noisy images and the effect is not good,in order to make full use of the local information of the image,a new image segmentation model combining global energy and local energy is proposed.At the same time,it also introduces the edge detection function into the new model.Experimental results show that the proposed method can be applied to the segmentation of many types of images.It is not only more efficient,but also more accurate.It can also be used when dealing with grayscale images that are significantly uneven and contain noisy images,Satisfactory segmentation effect can be get.In this paper,a level set image segmentation method based on wavelet edge extraction is proposed,and the segmentation effect of the new model is tested through experiments.Through the analysis of experimental results,the effectiveness of the proposed method is verified.