【摘要】：現(xiàn)代醫(yī)學(xué)成像技術(shù)中的磁共振成像(Magnetic Resonance Imaging,MRI)技術(shù)因?yàn)樗奶攸c(diǎn)使得其在臨床醫(yī)學(xué)方面的應(yīng)用需求不斷地增加。并且由于計(jì)算機(jī)科學(xué)技術(shù)的發(fā)展使醫(yī)學(xué)圖像更加廣泛的應(yīng)用于醫(yī)學(xué)生物研究和臨床醫(yī)學(xué)方面。然而,因?yàn)槟XMRI圖像中存在有部容積效應(yīng)、噪聲、灰度不均勻性和對(duì)比度低等缺陷,在實(shí)際應(yīng)用中腦MRI圖像的精確分割是十分困難的。據(jù)此,本文基于基礎(chǔ)模糊C均值聚類算法,針對(duì)MRI醫(yī)學(xué)圖像中存在的一些問題,對(duì)算法提出一定的改進(jìn),提出了一種基于各向異性濾波的模糊C均值聚類算法、進(jìn)行了灰度不均勻場(chǎng)的移除工作的研究并且應(yīng)用到實(shí)際腦MRI圖像的分割工作中去。本文主要做了以下幾方面的工作:(1)結(jié)合各向異性濾波去噪的方法提出了提出了一種基于各向異性濾波的模糊C均值聚類算法(PMFCM)。首先在本論文中研究分析基礎(chǔ)模糊聚類算法的構(gòu)成及其主要缺陷,隨后研究了現(xiàn)有算法的改進(jìn)思路,通過引入各向異性濾波方法對(duì)圖像進(jìn)行濾波得出的各中心點(diǎn)來代替空間鄰域項(xiàng)的構(gòu)造,不僅使得算法不用每次迭代時(shí)都計(jì)算其鄰域信息使得迭代時(shí)速度加快,并且由于采用各向異性濾波處理后的像素作為約束項(xiàng)的計(jì)算信息。本文算法可以有效的抵抗待分割圖像的噪聲,使得分割效果更加可靠,并且利用快速模糊C均值聚類算法思想,使得本文算法的運(yùn)算效率得到提高。(2)研究了含灰度不均勻場(chǎng)的腦MRI圖像的分割策略,并且將其引入到本文所提出的基于各向異性濾波的模糊C均值聚類算法中。使得本文算法可以有效的估計(jì)出圖像的灰度不均勻場(chǎng)的信息,利用在分割進(jìn)行的同時(shí)估計(jì)灰度不均勻場(chǎng)的方法,得到更加清晰的移除灰度不均勻場(chǎng)的分割后圖像。(3)研究了基于本文算法的聚類數(shù)目的初始化問題。在本文算法對(duì)圖像進(jìn)行各向異性濾波的前提下,利用二次差分的方法得到初始聚類中心和聚類數(shù)目,進(jìn)一步加快了本文算法的分割速度。(4)根據(jù)本文算法設(shè)計(jì)并實(shí)現(xiàn)了醫(yī)學(xué)圖像處理系統(tǒng)圖像分割子系統(tǒng)。
[Abstract]:Magnetic resonance imaging (Magnetic Resonance Imaging,MRI) technology in modern medical imaging technology has been increasing the demand for clinical applications because of its characteristics. Because of the development of computer science and technology, medical images are widely used in medical biological research and clinical medicine. However, due to the defects of partial volume effect, noise, gray inhomogeneity and low contrast in brain MRI images, it is very difficult to segment the brain MRI image accurately in practice. Therefore, based on the basic fuzzy C-means clustering algorithm, a fuzzy C-means clustering algorithm based on anisotropic filtering is proposed to solve some problems in MRI medical images. The work of removing the gray inhomogeneous field is studied and applied to the segmentation of the actual brain MRI image. The main work of this paper is as follows: (1) A fuzzy C-means clustering algorithm (PMFCM). Based on anisotropic filtering is proposed. In this paper, the composition of the basic fuzzy clustering algorithm and its main defects are studied, and then the improved ideas of the existing algorithms are studied. By introducing the anisotropic filtering method to replace the construction of spatial neighborhood terms, the algorithm not only computes its neighborhood information at every iteration, but also accelerates the iteration time. Due to the use of anisotropic filtering pixels as constraint information. This algorithm can effectively resist the noise of the image to be segmented, make the segmentation more reliable, and use the fast fuzzy C-means clustering algorithm. The computational efficiency of this algorithm is improved. (2) the segmentation strategy of brain MRI image with gray inhomogeneous field is studied and introduced into the proposed fuzzy C-means clustering algorithm based on anisotropic filtering. So that the algorithm can effectively estimate the information of the image gray inhomogeneous field, and use the method to estimate the gray level non-uniform field while the segmentation is going on. The segmented image with a clearer removal of the inhomogeneous gray field is obtained. (3) the initialization problem of the clustering number based on the proposed algorithm is studied. On the premise of anisotropic filtering of image in this paper, the initial clustering center and the number of clustering are obtained by using the method of quadratic difference. The segmentation speed of this algorithm is further accelerated. (4) the image segmentation subsystem of medical image processing system is designed and implemented according to this algorithm.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R445.2;TP391.41

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相關(guān)期刊論文 前1條

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本文編號(hào)：2221316