結(jié)合圖像結(jié)構(gòu)信息GVF Snake模型的圖像分割方法研究
發(fā)布時間：2018-10-10 14:25
【摘要】：圖像分割是一種將圖像中所包含的感興趣區(qū)域與背景區(qū)域分開,并且提取出目標(biāo)區(qū)域的圖像處理技術(shù)。作為一種關(guān)鍵性的基礎(chǔ)操作,圖像分割技術(shù)已經(jīng)成為圖像處理領(lǐng)域非常重要的研究內(nèi)容之一。主動輪廓(也稱為Snake)模型憑借其良好的分割特性,在圖像分割領(lǐng)域應(yīng)用廣泛。梯度矢量流(Gradient Vector Flow,GVF)主動輪廓模型改善了傳統(tǒng)Snake模型對初始輪廓線位置較為敏感的問題,并且對于凹型區(qū)域的分割性能也有所提升。GVF Snake模型作為一種經(jīng)典且有效的外力場改進模型也備受研究者關(guān)注。本文首先介紹了圖像分割的背景和意義,并且對圖像分割的概念進行了描述。然后介紹了幾種經(jīng)典的基礎(chǔ)分割方法;之后重點介紹了Snake模型和GVF Snake模型的分割方法,相比于Snake模型,GVF Snake外力場的作用范圍更大,分割效果更佳。然而GVF Snake模型在分割過程中也存在一些問題,針對這些不足之處,本文提出了兩種改進方法。(1)GVF Snake模型在分割帶有尖角的目標(biāo)時,曲線很難收斂到尖角處。針對這一問題,本文提出結(jié)合角點信息GVF Snake模型圖像分割方法。首先,運用基于邊緣輪廓曲率的角點檢測方法,檢測出圖像中的角點位置,并且在邊緣線上和角點處對GVF場進行局部修正,然后結(jié)合角點信息給出局部角點力,最后將角點力與修正后的GVF場相結(jié)合得出一種新的外力場。實驗證明,本文改進的GVF Snake模型能夠更好的收斂到圖像的尖角處。(2)GVF Snake模型相比于傳統(tǒng)的Snake模型在分割凹型邊界性能方面有了一定的提升。然而,對于深凹區(qū)域的分割,GVF Snake模型仍然很難收斂到深凹區(qū)域底部,并且GVF Snake模型對于噪聲的魯棒性以及邊緣保護方面也存在不足。針對這些問題,根據(jù)廣義GVF(Generized GVF,簡稱為GGVF)Snake模型,本文提出了基于圖像結(jié)構(gòu)信息各項異性GGVF(Image Structure Anisotropic GGVF,簡稱ISAGGVF)Snake模型。首先,求出圖像的結(jié)構(gòu)張量,然后根據(jù)圖像結(jié)構(gòu)張量構(gòu)建各項異性擴散矩陣。最后將GGVF Snake模型中的各項同性擴散替換成各項異性擴散矩陣。這樣外力場中的擴散項就是根據(jù)圖像的結(jié)構(gòu)信息自適應(yīng)調(diào)節(jié)擴散系數(shù)。實驗證明,本文改進的模型能夠準(zhǔn)確收斂到深凹底部,并且對于噪聲具有一定的魯棒性。
[Abstract]:Image segmentation is an image processing technique that separates the region of interest from the background region and extracts the target region. As a key basic operation, image segmentation technology has become one of the most important research contents in the field of image processing. Active contour (also known as Snake) model is widely used in image segmentation field because of its good segmentation characteristics. Gradient vector flow (Gradient Vector Flow,GVF) active contour model improves the sensitivity of the traditional Snake model to the position of the initial contour. And the segmentation performance of concave region is also improved. As a classical and effective improved model of external force field,. GVF Snake model has attracted much attention. This paper first introduces the background and significance of image segmentation, and describes the concept of image segmentation. Then several classical basic segmentation methods are introduced, and the segmentation methods of Snake model and GVF Snake model are emphasized. Compared with the Snake model, the external force field of, GVF Snake is larger and the segmentation effect is better than that of Snake model. However, there are some problems in the segmentation of GVF Snake model. In view of these shortcomings, two improved methods are proposed in this paper. (1) the curve of GVF Snake model is difficult to converge to the sharp angle when it is used to segment the target with sharp angle. In order to solve this problem, a method of image segmentation based on corner information GVF Snake model is proposed in this paper. Firstly, the corner position in the image is detected by using corner detection method based on the curvature of the edge contour, and the GVF field is locally corrected on the edge line and corner, and then the local corner force is given by combining the corner information. Finally, a new external force field is obtained by combining the corner force with the modified GVF field. Experimental results show that the improved GVF Snake model can converge better to the sharp corner of the image. (2) compared with the traditional Snake model, the performance of the) GVF Snake model is improved in the concave boundary segmentation. However, the segmented, GVF Snake model for deep concave region is still difficult to converge to the bottom of deep concave region, and the robustness of GVF Snake model to noise and edge protection are also insufficient. In order to solve these problems, according to the generalized GVF (Generized GVF, referred to as the GGVF) Snake model, this paper presents the heterosexual GGVF (Image Structure Anisotropic GGVF, ISAGGVF) Snake model based on the image structure information. Firstly, the structure Zhang Liang of the image is obtained, and then the heterosexual diffusion matrix is constructed according to the image structure Zhang Liang. Finally, the homogeneity diffusion in the GGVF Snake model is replaced by the heterosexual diffusion matrix. Thus the diffusion term in the external force field adaptively adjusts the diffusion coefficient according to the structure information of the image. Experimental results show that the improved model can converge to the deep concave bottom accurately and is robust to noise.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2262125
[Abstract]:Image segmentation is an image processing technique that separates the region of interest from the background region and extracts the target region. As a key basic operation, image segmentation technology has become one of the most important research contents in the field of image processing. Active contour (also known as Snake) model is widely used in image segmentation field because of its good segmentation characteristics. Gradient vector flow (Gradient Vector Flow,GVF) active contour model improves the sensitivity of the traditional Snake model to the position of the initial contour. And the segmentation performance of concave region is also improved. As a classical and effective improved model of external force field,. GVF Snake model has attracted much attention. This paper first introduces the background and significance of image segmentation, and describes the concept of image segmentation. Then several classical basic segmentation methods are introduced, and the segmentation methods of Snake model and GVF Snake model are emphasized. Compared with the Snake model, the external force field of, GVF Snake is larger and the segmentation effect is better than that of Snake model. However, there are some problems in the segmentation of GVF Snake model. In view of these shortcomings, two improved methods are proposed in this paper. (1) the curve of GVF Snake model is difficult to converge to the sharp angle when it is used to segment the target with sharp angle. In order to solve this problem, a method of image segmentation based on corner information GVF Snake model is proposed in this paper. Firstly, the corner position in the image is detected by using corner detection method based on the curvature of the edge contour, and the GVF field is locally corrected on the edge line and corner, and then the local corner force is given by combining the corner information. Finally, a new external force field is obtained by combining the corner force with the modified GVF field. Experimental results show that the improved GVF Snake model can converge better to the sharp corner of the image. (2) compared with the traditional Snake model, the performance of the) GVF Snake model is improved in the concave boundary segmentation. However, the segmented, GVF Snake model for deep concave region is still difficult to converge to the bottom of deep concave region, and the robustness of GVF Snake model to noise and edge protection are also insufficient. In order to solve these problems, according to the generalized GVF (Generized GVF, referred to as the GGVF) Snake model, this paper presents the heterosexual GGVF (Image Structure Anisotropic GGVF, ISAGGVF) Snake model based on the image structure information. Firstly, the structure Zhang Liang of the image is obtained, and then the heterosexual diffusion matrix is constructed according to the image structure Zhang Liang. Finally, the homogeneity diffusion in the GGVF Snake model is replaced by the heterosexual diffusion matrix. Thus the diffusion term in the external force field adaptively adjusts the diffusion coefficient according to the structure information of the image. Experimental results show that the improved model can converge to the deep concave bottom accurately and is robust to noise.
【學(xué)位授予單位】：江西理工大學(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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