本文選題：肝臟腫瘤分割 + 幾何形變模型�。� 參考：《山東師范大學》2017年碩士論文

【摘要】：CT圖像肝臟腫瘤分割,是肝癌等肝臟疾病計算機輔助檢測與診斷技術的基礎與關鍵,具有重要的研究意義和應用價值。目前已得到深入研究,并取得大量研究成果。其中,基于形變模型的分割方法得到了廣泛的運用。傳統(tǒng)幾何形變模型一般適用于對比度比較高的圖像,對于像CT圖像肝臟腫瘤這種具有灰度不均勻和低對比度特性的圖像,分割效果不是很好。針對這一問題,在傳統(tǒng)幾何形變模型的基礎上,提出了一種新的CT圖像肝臟腫瘤分割方法。所做的具體研究工作如下:(1)相關理論基礎的研究和算法的提出。仔細研究了CT圖像肝臟腫瘤的特點,包括灰度特性以及幾何特性。閱讀了大量有關CT圖像分割方法的文獻,對這些方法進行了仔細研究、分類總結,最終確定了本文的CT圖像肝臟腫瘤分割方法。(2)圖像預處理方法選擇。CT圖像由于獲取途徑的關系,具有一定的噪聲。如果直接對原始CT圖像進行分割,結果會不盡人意。所以根據去噪結果,確定了適合CT圖像肝臟腫瘤分割的預處理方式。(3)幾何形變模型改進思路的確定。為了獲得更好的分割結果,首先對預處理之后的圖像進行偏差估計和糾正,提高圖像質量。然后,基于CT圖像肝臟腫瘤的灰度不均勻以及周圍組織具有低對比度的特性,提出了一個局部強度聚類屬性,來說明圖像灰度的不均勻等級。在CT圖像肝臟腫瘤每個區(qū)域的周圍,設定一個局部聚類準則函數,作為分割區(qū)域周圍組織的核心,給定一個統(tǒng)一的分割標準。依據這一標準設置一個能量函數,其與周圍各個區(qū)域的能量函數以及代表肝臟腫瘤CT圖像灰度不均勻特性的偏向量場有關。最后,通過使能量函數最小化,實現對CT圖像感興趣區(qū)域的分割以及偏差估計與糾正。(4)分割后圖像優(yōu)化處理。為了獲得更好的分割效果,需要對分割后圖像進行優(yōu)化處理。針對分割后肝臟腫瘤的特點,選擇了閉運算的優(yōu)化方式。(5)實驗驗證。利用軟件開發(fā)平臺VS2010與Matlab R2010a以及輔助性軟件,對算法進行了驗證實驗,并進行了實驗結果的對比和量化分析,證明了算法的可行性和有效性。研究的創(chuàng)新之處是,(1)設定了本地強度聚類準則函數,可以更好地處理局部灰度不均勻的情況。(2)提出了雙向幾何形變模型能量函數,將演化方向設定為兩個方向,縮短了處理時間。研究的不足之處是,對于邊界變化比較多的圖像,迭代次數相對較多,分割時間沒有達到理想狀態(tài)。同時,算法對于對比度的敏感程度,還有待于增強。
[Abstract]:Ct image segmentation of liver tumors is the basis and key of computer aided detection and diagnosis of liver diseases such as liver cancer. It has important research significance and application value. At present, it has been deeply studied, and a large number of research results have been obtained. Among them, the segmentation method based on deformation model has been widely used. The traditional geometric deformation model is generally suitable for images with high contrast, but the segmentation effect is not very good for the images such as liver tumors in CT images, which have the characteristics of uneven grayscale and low contrast. In order to solve this problem, a new method of liver tumor segmentation in CT images is proposed based on the traditional geometric deformation model. The specific research work is as follows: 1) the theoretical basis and the algorithm. The characteristics of liver tumors in CT images, including grayscale and geometric characteristics, are carefully studied. Has read a lot of literature about CT image segmentation method, has carried on the careful research to these methods, classifies the summary, Finally, it is determined that the method of liver tumor segmentation in this paper, I. E. the preprocessing method of CT image, has some noise due to the way of obtaining it. If the original CT image is segmented directly, the result will be unsatisfactory. Therefore, according to the denoising results, the preprocessing method of liver tumor segmentation in CT image is determined. In order to obtain better segmentation results, the image after preprocessing is first estimated and corrected to improve the image quality. Then, based on the heterogeneity of liver tumors in CT images and the low contrast of surrounding tissues, a clustering attribute of local intensity is proposed to show the uneven grayscale of the images. A local clustering criterion function is set up around each region of liver tumor in CT image as the core of the tissue around the segmentation area and a unified segmentation criterion is given. According to this criterion, an energy function is set up, which is related to the energy function of the surrounding regions and the bias field which represents the heterogeneity of the gray level of the liver tumor CT image. Finally, by minimizing the energy function, the segmentation of the region of interest in CT images and the estimation and correction of the deviation are realized. In order to obtain a better segmentation effect, it is necessary to optimize the image processing after segmentation. According to the characteristics of segmented liver tumor, the closed operation optimization method. By using the software development platform VS2010, Matlab R2010a and auxiliary software, the algorithm is validated and compared with the experimental results. The feasibility and effectiveness of the algorithm are proved. The innovation of the study is that the local intensity clustering criterion function is set up, which can better deal with the local grayscale inhomogeneity. The energy function of the bidirectional geometric deformation model is proposed, and the evolution direction is set in two directions. The processing time is shortened. The disadvantage of the study is that the number of iterations is relatively large and the segmentation time is not up to the ideal state for the images with more boundary changes. At the same time, the sensitivity of the algorithm to contrast still needs to be enhanced.
【學位授予單位】：山東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R735.7;TP391.41

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