医学图像分割与融合算法研究

发布时间：2018-04-07 15:21

本文选题：图像融合　切入点：小波变换　出处：《吉林大学》2017年硕士论文

【摘要】：医学图像对于现代医学的意义随着可视化技术的发展变得越来越重要,其在临床诊断和科研方面也发挥着越来越重要的作用。所谓的医学成像,就是利用各种成像设备对人体内部的组织器官、脏器形态和功能变化等进行观察研究的方法。由于不同的医学成像设备所能提供的医学信息不同,得到的医学图像在临床诊断和治疗中所能发挥的作用也不同。融合不同成像设备所得到的医学图像与从医学图像中分割定位出特定区域对于医疗诊断有着至关重要的意义。本文主要针对图像融合与图像分割的发展情况以及目前比较常见的一些图像融合与图像分割方面的算法进行介绍、分析和对比,从而达到了解图像融合与分割相关内容,学习图像融合与分割的相关知识的目的,对图像融合与分割的发展过程、具体实现方法以及未来发展方向等有所认识和掌握,为以后的工作与学习奠定基础。本文针对医学图像处理中的图像融合和图像分割中的一些算法进行了研究和介绍,并针对现存算法的不足,对两种图像处理过程各提出了一种改进算法,并对图像融合的改进算法进行了实验,将改进算法的运行结果与其他方法的运行结果进行了对比验证,使改进算法的优越性直观的展示出来。在图像融合方面,对基于变换域的图像融合方法和基于空间域的融合方法进行简要的介绍,重点对一种小波变换与金字塔法相结合改进的融合算法进行了研究,并通过编程实验来验证该算法的实现效果。将通过不同设备的到的医学图像融合在一起,可以使不同医学图像中的信息融合在一幅图像中,这对于丰富组织和病灶的信息,对诊断精确性的提高有极大的帮助,对于治疗和医学研究有很大的促进作用。本文先对常见的基于空域的图像融合方法和基于变换域的图像融合方法进行了介绍和编程实现,然后又对基于小波变化的图像融合方法进行了介绍和编程实现,又对一种基于小波变换和金字塔法的改进的图像融合算法进行了说明和编程实现。通过几种算法的运行结果的比较,改进算法能够对参与融合的源图像信息进行充分的利用,使最终的能够信息,视觉效果好,能够很好地满足医学图像融合的需求。在图像分割方面,简单介绍了阈值分割方法、区域分割方法和边缘分割方法这些常用的分割算法,对水平集分割算法进行了深入研究,针对传统快速匹配方法的不足,在速度函数的计算中加入了灰度值,并通过对待分割图像进行预处理,可以很好的将待分割区域突出并分割出来,并基于以上分析提出了一种基于水平集算法的改进算法,分析并比较其对比普通水平集算法的优越性。
[Abstract]:The significance of medical image to modern medicine becomes more and more important with the development of visualization technology, and it also plays an increasingly important role in clinical diagnosis and scientific research.The so-called medical imaging is to use various imaging equipment to observe and study the changes of tissues and organs, organ morphology and function in human body.Because different medical imaging equipment can provide different medical information, the medical image can play a different role in clinical diagnosis and treatment.It is very important for medical diagnosis to fuse the medical images obtained by different imaging devices and to segment and locate specific regions from medical images.In this paper, the development of image fusion and image segmentation and some common algorithms of image fusion and image segmentation are introduced, analyzed and compared, so as to understand the relevant contents of image fusion and segmentation.The purpose of learning the relevant knowledge of image fusion and segmentation is to understand and master the development process of image fusion and segmentation, the concrete realization method and the future development direction, which will lay a foundation for future work and study.In this paper, some algorithms of image fusion and image segmentation in medical image processing are studied and introduced. In view of the shortcomings of existing algorithms, an improved algorithm is proposed for each of the two image processing processes.Experiments on the improved image fusion algorithm are carried out, and the results of the improved algorithm are compared with the results of other methods, so that the superiority of the improved algorithm can be displayed directly and intuitively.In the aspect of image fusion, the image fusion method based on transform domain and the fusion method based on spatial domain are briefly introduced, and an improved fusion algorithm based on wavelet transform and pyramid method is studied.And through programming experiments to verify the implementation of the algorithm.The fusion of medical images from different devices can make the information in different medical images fused into one image, which is of great help to enrich the information of tissues and lesions, and to improve the accuracy of diagnosis.For treatment and medical research has a great role in promoting.In this paper, the common image fusion method based on spatial domain and the image fusion method based on transform domain are introduced and programmed, then the image fusion method based on wavelet change is introduced and programmed.An improved image fusion algorithm based on wavelet transform and pyramid method is described and programmed.Through the comparison of the results of several algorithms, the improved algorithm can make full use of the source image information involved in the fusion, so that the final information can be achieved, the visual effect is good, and the medical image fusion needs can be well met.In the aspect of image segmentation, the threshold segmentation method, the region segmentation method and the edge segmentation method are introduced briefly. The level set segmentation algorithm is deeply studied, and the shortcomings of the traditional fast matching method are pointed out.The gray value is added to the calculation of the velocity function, and by preprocessing the segmented image, the region to be segmented can be well highlighted and segmented. Based on the above analysis, an improved algorithm based on the level set algorithm is proposed.The superiority of this algorithm over the ordinary level set algorithm is analyzed and compared.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41

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