本文關鍵詞：基于水平集的灰度不均勻圖像分割算法研究　出處：《大連海事大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 圖像分割 水平集 灰度不均勻圖像 活動輪廓

【摘要】：圖像分割技術是圖像處理領域中的一項重要研究內容,也是圖像分析與目標識別的重要步驟。迄今為止,國內外學者提出了多種圖像分割方法,然而由于圖像的復雜性和多樣性,圖像分割依然是一項重要而富有挑戰(zhàn)性的研究課題。在所有的圖像分割算法中,基于曲線演化理論的水平集算法是受到了極大的關注。其利用了輪廓曲線動態(tài)演化的思想,并且具有嚴謹的數學理論基礎,能夠解決很多其他分割方法難以解決的問題。本論文深入研究了一些經典的水平集模型,針對存在的缺陷和不足提出了一些改進,并最終能夠獲得理想的分割效果。具體的研究工作如下:(1)針對邊緣型水平集模型對曲線初始輪廓比較敏感的問題,給出了一種基于空間模糊聚類的邊緣型水平集分割模型。首先采用空間模糊聚類算法對圖像預分割,然后根據預分割的結果對邊緣型水平集演化模型中水平集函數進行初始化,并加入使用雙阱勢函數的距離規(guī)則項來避免在演化過程中水平集函數周期性初始化的問題。該算法引入了圖像空間域信息,克服了初始輪廓與參數均需要手動設定的缺點,并由于確定的初始位置,有效緩解了邊緣型模型對初始輪廓敏感的問題,使得分割結果更加準確。通過邊界十分模糊的乳腺腫塊圖像對該算法進行驗證。經過試驗驗證該算法能夠自動初始化并正確分割圖像。(2)針對LBF模型對初始輪廓比較敏感且容易陷入局部最優(yōu)的問題,給出了一種引入全局信息的局部區(qū)域型水平集分割模型。該模型將提供全局信息的C-V模型和提供局部信息的LBF模型通過局部熵結合起來,構建能量泛函,同時給出水平集演化的理論推導和數值求解。有效解決了 LBF模型對輪廓初始化敏感且容易陷入局部最優(yōu)的問題,同時也可以解決C-V模型不能處理灰度不均勻圖像的問題,并且可自動設置權重。最后通過灰度不均勻圖像驗證該算法的有效性。(3)針對LIC模型對圖像修正的偏置場沒有實質性約束(偏置場平滑且緩慢變化),導致偏置場修正結果以及圖像分割結果不是十分理想的問題,給出了一種基于乘法優(yōu)化的局部聚類水平集圖像分割模型。通過一組平滑的線性基函數對偏置場進行擬合,以在理論上保證偏置場的光滑性。將圖像分割與偏置場修正融合在一個能量泛函框架中,并給出水平集演化方程的理論推導和數值求解。該算法有效提高了 LIC模型的分割精度,并對偏置場進行了約束。最后通過合成圖像與醫(yī)學灰度不均勻圖像進行試驗,驗證了該算法的有效性。
[Abstract]:Image segmentation is an important research content in the field of image processing, and it is also an important step in image analysis and target recognition. So far, many kinds of image segmentation methods have been proposed by domestic and foreign scholars. However, due to the complexity and diversity of images, image segmentation is still an important and challenging research topic. The level set algorithm based on curve evolution theory has attracted much attention. It makes use of the idea of dynamic evolution of contour curve and has a rigorous mathematical theory foundation. Can solve many other segmentation methods difficult to solve. This paper in-depth study of some classical level set model, aiming at the shortcomings and shortcomings of some improvements. Finally, the ideal segmentation effect can be obtained. The specific research work is as follows: 1) aiming at the problem that the edge level set model is more sensitive to the initial contour of the curve. An edge level set segmentation model based on spatial fuzzy clustering is presented. Firstly, the spatial fuzzy clustering algorithm is used to presegment the image. Then the level set function in the evolution model of edge level set is initialized according to the result of pre-segmentation. The distance rule term of the double well potential function is added to avoid the problem of periodic initialization of the level set function in the evolution process. The algorithm introduces the spatial domain information of the image. It overcomes the shortcoming that the initial contour and parameters need to be set manually, and because the initial position is determined, the problem that the edge model is sensitive to the initial contour is effectively alleviated. The segmentation result is more accurate. The algorithm is verified by the breast mass image with very fuzzy boundary. The experiment shows that the algorithm can initialize automatically and segment the image correctly. In order to solve the problem that LBF model is sensitive to initial contour and is prone to fall into local optimum. In this paper, a local region-type horizontal set segmentation model with global information is presented, which combines the C-V model with the local information model and the LBF model with local information through local entropy. The energy functional is constructed, and the theoretical derivation and numerical solution of the level set evolution are given, which effectively solves the problem that the LBF model is sensitive to contour initialization and is prone to fall into local optimum. At the same time, it can also solve the problem that C-V model can not deal with uneven grayscale images. And the weight can be set automatically. Finally, the validity of the algorithm is verified by grayscale non-uniform image. (3) there is no material constraint (smooth and slow change of bias field) for the offset field modified by LIC model. The results of bias field correction and image segmentation are not very ideal. A local clustering level set image segmentation model based on multiplication optimization is presented. A set of smooth linear basis functions is used to fit the bias field. In order to ensure the smoothness of bias field theoretically, image segmentation and offset field correction are fused into an energy functional framework. The theoretical derivation and numerical solution of the evolution equation of the level set are given. The algorithm improves the segmentation accuracy of the LIC model effectively. The bias field is constrained. Finally, the validity of the proposed algorithm is verified by the experiments of synthetic images and medical grayscale non-uniform images.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：1393186