本文選題：圖像分割 + 水平集方法��； 參考：《中央民族大學(xué)》2017年碩士論文

【摘要】：隨著計算機(jī)科學(xué)技術(shù)的發(fā)展,數(shù)字圖像被廣泛應(yīng)用于各個領(lǐng)域,利用計算機(jī)進(jìn)行圖像分割是將圖像中的有用信息提取出來,從而對相關(guān)信息進(jìn)行分析。水平集方法在圖像分割和計算機(jī)視覺領(lǐng)域有很廣泛的應(yīng)用,具有非常好的分割性能。本文對常用的水平集分割模型:蛇模型、GAC模型、M-S模型、C-V模型、LBF模型的基本分割原理、模型的構(gòu)建、模型的優(yōu)缺點(diǎn)進(jìn)行了全面的分析和介紹,并針對其存在的問題進(jìn)行了改進(jìn)。在傳統(tǒng)的水平集方法中,水平集函數(shù)需要保持符號距離函數(shù),而現(xiàn)有的模型均需要對水平集函數(shù)進(jìn)行重新初始化,使其保持符號距離函數(shù),這樣會引起數(shù)值計算的錯誤,最終破壞演化的穩(wěn)定性。另外,部分模型只適用于灰度值較為均勻的圖像,對灰度值不均勻的圖像不能進(jìn)行理想的分割。針對這些問題,本文結(jié)合C-V模型和LBF模型的思想,提出了兩種新的圖像分割模型。1.新型的四相水平集圖像分割模型,該模型結(jié)合了 C-V模型的思想,應(yīng)用兩個水平集函數(shù)對灰度不均勻的圖像進(jìn)行分割,特別是其正則項被定義為一個雙勢函數(shù),具有向前向后擴(kuò)散的作用,使水平集函數(shù)在演化過程中保持為符號距離函數(shù),避免了重新初始化的過程。最后對該模型進(jìn)行數(shù)值仿真,對簡單的cube圖,Brain圖,子宮肌瘤CT圖等進(jìn)行分割,實驗表明了新模型能夠更好的分割灰度不均勻的圖像,輪廓清晰,計算速度較快,充分證明了新模型的可操作性和有效性。2.新型的LBF模型,該模型結(jié)合了 LBF模型的思想和C-V模型的思想,利用局部和全局的信息對圖像進(jìn)行分割,該模型的正則項仍然定義為一個雙勢函數(shù),確保水平集函數(shù)在演化過程中保持為符號距離函數(shù),應(yīng)用變分水平集方法,通過雙勢函數(shù)得到最小化能量泛函的梯度下降流方程。最后對其進(jìn)行數(shù)值仿真,實驗發(fā)現(xiàn),新模型能夠?qū)?fù)雜的灰度不均勻的醫(yī)學(xué)圖像進(jìn)行很好的分割,且利用雙勢函數(shù)的優(yōu)點(diǎn),避免了水平集函數(shù)的重新初始化過程,使其時刻保持為符號距離函數(shù),充分證明了新模型的可行性。
[Abstract]:With the development of computer science and technology, digital image is widely used in various fields. Image segmentation by computer is to extract the useful information from the image and analyze the relevant information. Level set method is widely used in image segmentation and computer vision. In this paper, the basic segmentation principle, the construction of the model, the advantages and disadvantages of the LBF model are analyzed and introduced in detail, and the existing problems are improved according to the common horizontal set segmentation model: the serpent GAC model and the M-S model and the C-V model and the LBF model. In the traditional level set method, the level set function needs to maintain the symbolic distance function, but the existing models need to reinitialize the level set function to keep the symbolic distance function, which will lead to the error of numerical calculation. Finally destroy the stability of evolution. In addition, part of the model is only suitable for the image with more uniform gray value, and the image with uneven gray value can not be segmented perfectly. In order to solve these problems, two new image segmentation models, I. e., C-V model and LBF model, are proposed in this paper. A new four-phase horizontal set image segmentation model, which combines the idea of C-V model, uses two level set functions to segment an image with uneven grayscale, especially when its regular term is defined as a double potential function. It has the function of forward and backward diffusion, which keeps the level set function as the symbolic distance function in the evolution process, thus avoiding the process of reinitialization. Finally, the numerical simulation of the model is carried out, and the simple cube diagram brain map, hysteromyoma CT image and so on are segmented. The experiment shows that the new model can segment the uneven gray image better, the contour is clear, and the calculation speed is faster. The maneuverability and validity of the new model are fully proved. A new LBF model, which combines the idea of LBF model with the idea of C-V model, uses local and global information to segment the image. The regular term of the model is still defined as a double potential function. It is ensured that the level set function remains a signed distance function in the evolution process. By using the variational level set method, the gradient descent flow equation for minimizing the energy functional is obtained by using the double potential function. Finally, the numerical simulation results show that the new model can segment the medical image with complex grayscale heterogeneity and avoid the reinitialization of the level set function by using the advantage of the double potential function. The new model is proved to be feasible by keeping it as a symbolic distance function.
【學(xué)位授予單位】：中央民族大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 王海軍;柳明;;克服灰度不均勻性的腦MR圖像分割及去偏移場模型[J];山東大學(xué)學(xué)報(工學(xué)版);2011年03期

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本文編號：2054147