基于高斯混合模型的醫(yī)學(xué)圖像分割技術(shù)研究

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摘要 醫(yī)學(xué)圖像分割算法的研究是當(dāng)前醫(yī)學(xué)圖像處理和分析的熱點(diǎn)，醫(yī)學(xué)圖像分割主要是指將圖像分成各具特性的區(qū)域并提取出感興趣目標(biāo)的技術(shù)。由于醫(yī)學(xué)應(yīng)用對(duì)醫(yī)學(xué)圖像分割的準(zhǔn)確度和分類算法的速度要求較高，而人體解剖的個(gè)體差異較大，一般來(lái)說(shuō)由于圖像本身受到噪聲、局部體效應(yīng)以及偏移場(chǎng)效應(yīng)等的影響，往往使得一般意義上的圖像分割算法并不能達(dá)到理想的效果。其中高斯混合模型越來(lái)越受到人們的關(guān)注，已經(jīng)慢慢成為是具有代表性的一種聚類分割方法。期望最大化(Expectation Maximization，EM)算法為模型參數(shù)提供了一種簡(jiǎn)單有效的極大似然迭代求解方法，本論文主要分以下幾個(gè)方面對(duì)基于高斯混合模型的圖像分割進(jìn)行了研究討論。
1.對(duì)國(guó)內(nèi)外醫(yī)學(xué)圖像分割方法進(jìn)行了介紹，介紹了高斯混合模型及EM算法以及EM算法在高斯混合模型的應(yīng)用，并重點(diǎn)對(duì)基于高斯混合模型的圖像分割方法進(jìn)行了分析。闡述并展示了一個(gè)具體的數(shù)據(jù)集，為論文的深入研究提供基礎(chǔ)。
2.介紹了MATLAB語(yǔ)言的特點(diǎn)以及如何利用MATLAB及其圖像處理工具箱進(jìn)行圖像分割。數(shù)字圖像的信息量很大，對(duì)處理速度的要求也很高，而MATLAB的基本數(shù)據(jù)單位是矩陣，故常用MATLAB進(jìn)行圖像處理。
3.在迭代的過(guò)程中貝葉斯后驗(yàn)概率的干擾，以及醫(yī)學(xué)圖像信息本身的復(fù)雜性和受到的影響，而EM算法雖然簡(jiǎn)單容易理解，但仍有不足之處。針對(duì)基本的EM算法存在的問(wèn)題，我們要想辦法減少對(duì)初始值的依賴和計(jì)算出的貝葉斯后驗(yàn)概率對(duì)模型分量間的干擾，故論文引入了FCM算法以解決這個(gè)問(wèn)題。實(shí)驗(yàn)結(jié)果表明，該方法對(duì)算法收斂頗有效果，在大部分情況下都得到了較好的分割精度，有效提高圖像分割整體性能。

關(guān)鍵詞：圖像分割 高斯混合模型 EM算法 FCM算法。


Medical Image Segmentation Based on Gaussian Mixture
Model

ABSTRACT Research Summary medical image segmentation algorithm is still current hot medical image processing and analysis , medical image segmentation mainly refers to an image into regions , each with features and technology to extract the object of interest . However, due to the large individual differences in human anatomy , and medical applications to speed medical image segmentation and classification accuracy of the algorithm are higher, and because the image itself is affected by noise, offset and local body effects such as field-effect , making the segmentation algorithm nearly did not achieve the desired scattered fruit . Which is a Gaussian mixture model clustering segmentation Taiwan representative methods , more and more people's attention. Expectation-maximization (Expectation Maximization, EM) algorithm for the model parameters to provide a simple and effective method for solving ML chosen generations , the paper image segmentation Gaussian mixture model-based units were studied .
     1 .for domestic and foreign medical image segmentation methods Dingjie Shao , focusing on the image segmentation method based on Gaussian mixture model -depth analysis . Zhou Shao T Gaussian mixture model and EM algorithm described in the application of the EM algorithm Gaussian mixture model , and shows a real guillotine specific datasets for further research in this paper provides a foundation.
2.introduces the MATLAB language features and how to use MATLAB and Image Processing Toolbox for image segmentation. A large amount of information digital images, processing speed requirements are high, and MATLAB basic data unit is the matrix, it is commonly used MATLAB for image processing.
      3. Interference in the process of iteration Bayesian posterior probabilities, and the complexity and impact of medical image information itself, the EM algorithm is simple and easy to understand, but there are still shortcomings. For the basic problems of the EM algorithm, we find a way to reduce dependence on the initial value and calculate the Bayesian posterior probability of interference between model components, so the paper introduces the FCM algorithm to solve this problem. Experimental results show that the algorithm converges quite effective method, in most cases have been better segmentation accuracy, improve the overall performance of image segmentation.

Keywords : Image segmentation stick Gaussian model EM algorithm algorithm FCM.


目錄

摘要 I
ABSTRACT II
第一章 緒論 1
1.1 課題研究背景及意義 1
1.2 國(guó)內(nèi)外研究現(xiàn)狀 1
1.3 存在問(wèn)題 2
1.4 論文的內(nèi)容安排 3
第二章 高斯混合模型及EM算法 4
2.1 高斯混合模型概述 4
2.1.1 單高斯模型 4
2.1.2 高斯混合模型 4
2.2 EM算法 7
2.2.1 概述 7
2.2.2 算法原理 7
2.2.3 算法性質(zhì) 8
2.2.4 采用EM估計(jì)GMM的參數(shù) 9
2.2.5 EM算法在高斯混合模型的應(yīng)用 10
2.3 本章小結(jié) 11
第三章 MATLAB基本知識(shí)介紹 12
3.1　MATLAB概述 12
3.2　MATLAB語(yǔ)言的特點(diǎn) 12
3.3　MATLAB在圖像處理中的應(yīng)用 13
3.4 本章小結(jié) 13
第四章 高斯混合模型醫(yī)學(xué)圖像分割方法 14
4.1 圖像的幾何模糊性 14
4.2 模糊C均值聚類方法 15
4.4 實(shí)驗(yàn)結(jié)果 18
4.5 本章小結(jié) 20
第五章 總結(jié)與展望 21
5.1 總結(jié) 21
5.2 展望 21
致謝 23
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