Horizontal Set Image Segmentation And Its Application In Medical Image Processing

Image segmentation is a basic technology in the field of computer vision and image processing.The segmentation technology lays the foundation for subsequent image analysis.Due to the widespread problems of gray unevenness,noise,and weak edges,how to effectively segment images has always been a difficult problem in the field of image processing.The level set method in the active contour model is simple in numerical calculation.It mainly uses the curvature and normal vector of the curve to evolve the level set function.It can realize the change of the topological structure of the object naturally.In recent years,researchers have also applied level set segmentation models to medical image segmentation.This article mainly discusses the level set segmentation model,and applies the level set segmentation to the segmentation of the anterior segment image and the iris image.Main contents:First,this article briefly introduces the research background of image segmentation and the level set image segmentation model,and elaborates the current research status of level set segmentation methods at home and abroad.Secondly,the theory of curve evolution and CV model are introduced in detail.In view of the coincidence of the intensity range between different object areas,it is difficult to segment the image when the intensity is uneven.This paper proposes a new level set segmentation method.Based on the non-uniform image model,the optimal segmentation plane of the image domain is derived.On the plane,a new region-based pressure function is proposed,and an energy functional is defined in the level set formula.By minimizing the energy function,the non-uniform image is segmented and the bias field is estimated.In addition,in order to accurately estimate the bias field,a new adaptive scale parameter is designed for the kernel function.Finally,simulation experiments are performed on real and synthetic images,and the experimental results show that the method is superior in terms of accuracy,efficiency,and robustness.Then,the Hough transform algorithm is introduced in detail.Because the traditional Hough transform based on circle detection locates the human eye iris,it involves three-dimensional parameter space,so there are shortcomings such as considerable computational time and space overhead.Aiming at this problem,a Hough transform circle detection algorithm that uses gradients to reduce theparameter space dimension is proposed.First,the image is pre-processed with mathematical morphology to reduce noise and eyelash interference.Secondly,the CV model is used to parameterize the poles based on the energy function.On the principle of small value,the inner contour of the iris is obtained with the help of image force,which makes the positioning result accurate.Finally,the outer contour of the iris is located by using the improved Hough transform.The high-quality and low-quality human eye iris images were used to simulate the improved algorithm.The results show that this method not only improves the positioning speed,but also significantly improves the positioning accuracy.Compared with other methods,the image quality requirements are also significantly reduced.Finally,both methods are compared experimentally with existing segmentation methods,and the results show that the segmentation results of the proposed algorithm are superior to other segmentation algorithms in both subjective and objective evaluation indicators.