本文選題：圖像分割 + 活動輪廓模型��； 參考：《西北農(nóng)林科技大學(xué)》2017年碩士論文

【摘要】：圖像分割作為數(shù)字圖像分析的重要步驟之一。在實(shí)際的醫(yī)學(xué)圖像中,觀測圖像中經(jīng)常存在復(fù)雜噪聲、灰度不均勻以及低對比度的問題,使得精確、快速地分割圖像面臨諸多難題。而活動輪廓模型以其自適應(yīng)性、亞像素精度等優(yōu)點(diǎn)成為研究的熱點(diǎn)。本文針對以活動輪廓模型為基礎(chǔ)的圖像分割方法進(jìn)行了相對深入的探討。首先對傳統(tǒng)的活動輪廓模型進(jìn)行了分類歸納和綜述,然后重點(diǎn)介紹以區(qū)域信息為的幾何活動輪廓模型的數(shù)學(xué)背景以及幾個典型區(qū)域活動輪廓模型。最后對加速局部區(qū)域信息活動輪廓模型的收斂速度進(jìn)行了重點(diǎn)研究,最終提出了一種基于活動輪廓模型的兩步快速分割算法,主要內(nèi)容如下:1.基于局部區(qū)域信息的活動輪廓模型可以抵抗未知復(fù)雜噪聲的影響,對灰度非同質(zhì)圖像可以達(dá)到較好的分割效果,但模型在計(jì)算時采取梯度下降法求解,因而計(jì)算復(fù)雜度高、收斂速度緩慢,同時由于僅利用局部區(qū)域信息,使得模型對設(shè)定的初始輪廓敏感。本文提出并采用兩步分割算法。第一步:采用下采樣減少數(shù)據(jù)量,對采樣后的圖像進(jìn)行分割,得到粗分割結(jié)果。第二步:將粗分割結(jié)果上采樣到原始圖像的比例,并作為細(xì)分割的初始輪廓,進(jìn)行精細(xì)分割。研究結(jié)果表明,與一般的區(qū)域活動輪廓模型相比,兩步分割模型由于第一步分割提供了較好的初始值,能夠在極少的步數(shù)內(nèi)得到更精確的結(jié)果。2.第一步分割得到的粗輪廓上采樣到原始圖像的比例,分割結(jié)果與真實(shí)的目標(biāo)邊界很接近,但仍有一定的差距。為保證第二步分割水平集函數(shù)的演化穩(wěn)定、快速地進(jìn)行,引入距離正則化能量泛函。在第二步分割的模型中引入一個距離函數(shù)dR,它能夠校正模型與初始輪廓的偏差,保證曲線演化的穩(wěn)定性,該距離函數(shù)是連接兩步分割的關(guān)鍵。3.梯度下降流法是以函數(shù)當(dāng)前點(diǎn)對應(yīng)的梯度(或近似梯度)的反方向的規(guī)定步長距離點(diǎn)進(jìn)行迭代探索,目前應(yīng)用范圍廣,但其收斂速度慢,使得模型迭代過程長。本文算法中采用Bregman迭代算法快速求解,有效地提高了模型的分割效率。采用MATLAB軟件進(jìn)行大量仿真實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明,與CV模型、LBF模型、LIF模型以及LCK模型對比,本文算法能夠在極少的迭代次數(shù)內(nèi)分割非同質(zhì)圖像和噪聲圖像。
[Abstract]:Image segmentation is one of the important steps in digital image analysis. In actual medical images, the problems of complex noise, uneven gray scale and low contrast often exist in the observed images, which make the accurate and fast segmentation of images face many difficulties. The active contour model has become a hot research area because of its adaptability and sub-pixel accuracy. In this paper, the method of image segmentation based on active contour model is discussed. Firstly, the traditional active contour model is classified and summarized, then the mathematical background of geometric active contour model based on region information and several typical regional active contour models are introduced. Finally, a two-step fast segmentation algorithm based on active contour model is proposed. The main contents are as follows: 1. The active contour model based on local region information can resist the influence of unknown complex noise, and achieve better segmentation effect for gray non-homogeneous image. However, the model is solved by gradient descent method, so the computational complexity is high. The convergence rate is slow, and the model is sensitive to the initial profile because only the local region information is used. In this paper, a two-step segmentation algorithm is proposed and used. The first step is to use downsampling to reduce the amount of data, and then segment the sampled image to obtain coarse segmentation results. The second step: the coarse segmentation result is sampled to the original image scale, and as the initial contour of fine segmentation, fine segmentation is carried out. The results show that the two-step segmentation model can get more accurate results in a few steps because the first step segmentation provides a better initial value than the general regional active contour model. In the first step, the ratio of the coarse contour sampled to the original image is very close to the real target boundary, but there is still a certain gap between the segmentation results and the real target boundary. In order to ensure the evolution stability of the second step partition level set function, the distance regularization energy functional is introduced. A distance function, dR, is introduced into the model of the second step segmentation, which can correct the deviation between the model and the initial contour and ensure the stability of the curve evolution. The distance function is the key to connect the two-step segmentation. Gradient descent flow method is an iterative exploration based on the specified step length distance point of the gradient (or approximate gradient) corresponding to the current point of the function. At present, it has a wide range of applications, but its convergence rate is slow, so that the iterative process of the model is long. In this paper, Bregman iterative algorithm is used to solve the problem quickly, which improves the efficiency of model segmentation. A large number of simulation experiments are carried out with MATLAB software. The experimental results show that compared with the CV model, the LBF model, the LIF model and the LCK model, the proposed algorithm can segment heterogeneous and noisy images in a few iterations.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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