文档介绍：数字图像的多分辨率分析处理方法研究
—基于小波变换的医学图像分割的研究
电信学院电子信息工程专业
摘要
图像分割是一种重要的图像分析技术。对图像分割的研究一直是图像技术研究中的热点和焦点。医学图像分割是图像分割的一个重要应用领域,也是一个经典难题,至今已有上千种分割方法,既有经典的方法也有结合新兴理论的方法。
本论文首先介绍了双峰法以及最大类方差自动阈值法,然后重点介绍一种基于小波变换的图像分割方法,该方法先对图像的灰度直方图进行小波多尺度变换,然后从较大的尺度系数到较小的尺度系数逐步定位出灰度阈值。最后,对这几种算法的分割效果进行了比较。实验结果表明, 本设计能够实时稳定的对目标分割提取,分割效果良好。
医学图像分割是医学图像处理中的一个经典难题。图像分割能够自动或半自动描绘出医学图像中的解剖结构和其它感兴趣的区域,从而有助于医学诊断。
关键词:小波变换;图像分割;阈值
Abstract
The image segmentation is an important technology of image processing. It is still a hot point and focus of image image segmentation is an important application in the field of image segmentation, and it is also a classical difficult problem for researchers. Thousands of methods have been put forward to medical image segmentation. Some use classical methods and others use new methods.
In this paper , first introduced the petronas method and maximum between class variance .Then focus introduced a method of image segmentation based on wavelet transform is discussed. In this method, the wavelet multiscale transform of image gray histogram is done first .Moreover , the gray threshold is gradually found out from large scale coefficients to small scale coefficients. Finally,the effects of the methods in segmentation pared . The experimental results indicate that the system can obtain a good performance of image segmentation.
Medical image segmentation is a classical puzzle for researchers. Image segmentation is the method to delineate anatomic structures or other interested regions automatically or semi-automatically, which is helpful to diagnosis and plays a crucial role in many medical imaging applications.
Key words: Wavelet Transform; Image Segmentation;threshold
目录
第一章绪论 1
图像分割技术的现状和发展情况 1
图像分割主要研究方法 1
边缘检测法 2
区域提取法 2
阈值分割法 3
结合特定理论工具的分割法 3
论文的内容与结构安排 ...4
第二章图像分割预处理 5
图像平滑 5
中值滤波原理 5
平滑效果分析 6
灰度调整 7
灰度调整原理 7
灰度调整效果分析 7
本章小结 8
第三章基于阈值的图像分割技术 9
阈值分割原理