本文選題：肝臟腫瘤分割 + 幾何形變模型　； 參考：《山東師范大學(xué)》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(jì)算機(jī)輔助檢測(cè)與診斷技術(shù)的基礎(chǔ)與關(guān)鍵,具有重要的研究意義和應(yīng)用價(jià)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(yùn)用。傳統(tǒng)幾何形變模型一般適用于對(duì)比度比較高的圖像,對(duì)于像CT圖像肝臟腫瘤這種具有灰度不均勻和低對(duì)比度特性的圖像,分割效果不是很好。針對(duì)這一問(wèn)題,在傳統(tǒng)幾何形變模型的基礎(chǔ)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(guān)理論基礎(chǔ)的研究和算法的提出。仔細(xì)研究了CT圖像肝臟腫瘤的特點(diǎn),包括灰度特性以及幾何特性。閱讀了大量有關(guān)CT圖像分割方法的文獻(xiàn),對(duì)這些方法進(jìn)行了仔細(xì)研究、分類(lèi)總結(jié),最終確定了本文的CT圖像肝臟腫瘤分割方法。(2)圖像預(yù)處理方法選擇。CT圖像由于獲取途徑的關(guān)系,具有一定的噪聲。如果直接對(duì)原始CT圖像進(jìn)行分割,結(jié)果會(huì)不盡人意。所以根據(jù)去噪結(jié)果,確定了適合CT圖像肝臟腫瘤分割的預(yù)處理方式。(3)幾何形變模型改進(jìn)思路的確定。為了獲得更好的分割結(jié)果,首先對(duì)預(yù)處理之后的圖像進(jìn)行偏差估計(jì)和糾正,提高圖像質(zhì)量。然后,基于CT圖像肝臟腫瘤的灰度不均勻以及周?chē)M織具有低對(duì)比度的特性,提出了一個(gè)局部強(qiáng)度聚類(lèi)屬性,來(lái)說(shuō)明圖像灰度的不均勻等級(jí)。在CT圖像肝臟腫瘤每個(gè)區(qū)域的周?chē)?設(shè)定一個(gè)局部聚類(lèi)準(zhǔn)則函數(shù),作為分割區(qū)域周?chē)M織的核心,給定一個(gè)統(tǒng)一的分割標(biāo)準(zhǔn)。依據(jù)這一標(biāo)準(zhǔn)設(shè)置一個(gè)能量函數(shù),其與周?chē)鱾�(gè)區(qū)域的能量函數(shù)以及代表肝臟腫瘤CT圖像灰度不均勻特性的偏向量場(chǎng)有關(guān)。最后,通過(guò)使能量函數(shù)最小化,實(shí)現(xiàn)對(duì)CT圖像感興趣區(qū)域的分割以及偏差估計(jì)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(duì)分割后圖像進(jìn)行優(yōu)化處理。針對(duì)分割后肝臟腫瘤的特點(diǎn),選擇了閉運(yùn)算的優(yōu)化方式。(5)實(shí)驗(yàn)驗(yàn)證。利用軟件開(kāi)發(fā)平臺(tái)VS2010與Matlab R2010a以及輔助性軟件,對(duì)算法進(jìn)行了驗(yàn)證實(shí)驗(yàn),并進(jìn)行了實(shí)驗(yàn)結(jié)果的對(duì)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(shè)定了本地強(qiáng)度聚類(lèi)準(zhǔn)則函數(shù),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(shù),將演化方向設(shè)定為兩個(gè)方向,縮短了處理時(shí)間。研究的不足之處是,對(duì)于邊界變化比較多的圖像,迭代次數(shù)相對(duì)較多,分割時(shí)間沒(méi)有達(dá)到理想狀態(tài)。同時(shí),算法對(duì)于對(duì)比度的敏感程度,還有待于增強(qiáng)。
[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(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：R735.7;TP391.41

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