發(fā)布時間：2018-01-31 23:19

  本文關(guān)鍵詞： 圖像分割 顱內(nèi)出血 圖割 超體素 半監(jiān)督　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：顱內(nèi)出血(ICH)是最嚴(yán)重的急性腦血管疾病之一,也是急性神經(jīng)紊亂疾病,如偏癱等的重要發(fā)病誘因。對于臨床治療來說,顱內(nèi)出血的分割具有重要意義。因此,如何應(yīng)用CT圖像診斷顱內(nèi)出血已成為了腦醫(yī)學(xué)領(lǐng)域最熱門的研究課題之一。在傳統(tǒng)的醫(yī)學(xué)圖像分析中,醫(yī)學(xué)從業(yè)人員主要依靠手工測繪以及自身的經(jīng)驗,通過估計得到對于顱內(nèi)出血情況的判斷。圖像分割技術(shù)的引入,大大減輕了醫(yī)學(xué)從業(yè)人員的工作負(fù)擔(dān),分割得到的量化數(shù)據(jù)也為醫(yī)學(xué)從業(yè)人員提供了精確的診斷依據(jù)。近年來各種圖像分割算法接連提出,其中基于超像素,圖論和半監(jiān)督學(xué)習(xí)的圖像分割算法,由于其良好的分割性能已經(jīng)成為眾多研究人員關(guān)注的熱點。超像素算法通過依據(jù)像素之間特征的相似性將圖像劃分為小區(qū)域,減少了圖像中的冗余信息,在很大程度上降低了后續(xù)圖像處理過程的復(fù)雜度�；趫D論的圖像分割算法則通過將圖像特性對應(yīng)于圖論特性,將圖像分割問題轉(zhuǎn)化為網(wǎng)絡(luò)圖的分割問題,通過將圖像的全局分割與局部信息處理相結(jié)合,減少了'圖像離散化造成的誤差,從而可獲得良好的分割結(jié)果。在有標(biāo)簽數(shù)據(jù)稀缺的情況下,基于半監(jiān)督學(xué)習(xí)的圖像分割方法可以利用大量的無標(biāo)簽數(shù)據(jù)增強分割結(jié)果。本文圍繞顱內(nèi)出血區(qū)域分割以及超像素的特性展開研究,重點研究了基于超像素的圖割算法和基于超像素的Tri-training算法在顱內(nèi)出血區(qū)域分割領(lǐng)域的應(yīng)用。本文的主要工作和貢獻如下:1、研究了圖像分割領(lǐng)域中常用的算法及各自的應(yīng)用范圍,簡要介紹了醫(yī)學(xué)圖像分割的特點以及顱內(nèi)出血(ICH)的發(fā)病原理以及CT成像特點;2、詳細(xì)介紹了超像素算法的分類,基本原理以及優(yōu)缺點,基于現(xiàn)有的超像素算法,結(jié)合顱內(nèi)出血區(qū)域分割這一應(yīng)用場景,提出了一種新的超體素算法;3、詳細(xì)介紹了圖割算法的基本原理以及優(yōu)缺點,針對基于圖割的圖像分割算法中人工參與和模型估計不足的問題,提出了一種基于高斯混合模型(GMM)的有監(jiān)督圖割算法。該算法根據(jù)醫(yī)學(xué)圖像的特性,利用已有的有標(biāo)簽數(shù)據(jù)為先驗知識,通過GMM算法建立前景及背景的模型,使得基于圖割的圖像分割算法能夠?qū)崿F(xiàn)全自動分割。4、詳細(xì)介紹了 Tri-training算法的基本原理以及優(yōu)缺點。結(jié)合超體素的特性,針對醫(yī)學(xué)圖像分割中有標(biāo)簽樣本獲取困難這一情況,提出了一種基于超體素和Tri-training算法的顱內(nèi)出血區(qū)域分割算法。該算法利用醫(yī)學(xué)圖像處理領(lǐng)域中存在的少量有標(biāo)簽數(shù)據(jù)和大量無標(biāo)簽數(shù)據(jù),實現(xiàn)了醫(yī)學(xué)圖像的自動分割。
[Abstract]:Intracranial hemorrhage (ICH) is one of the most serious acute cerebrovascular diseases, and is also an important cause of acute neurological disorders, such as hemiplegia. The segmentation of intracranial hemorrhage is of great significance. Therefore, how to diagnose intracranial hemorrhage with CT image has become one of the hottest research topics in the field of brain medicine. Medical practitioners mainly rely on manual mapping and their own experience, through the estimation of intracranial hemorrhage, the introduction of image segmentation technology, greatly reduce the workload of medical practitioners. In recent years, a variety of image segmentation algorithms have been proposed one after another, which is based on super-pixel, graph theory and semi-supervised learning image segmentation algorithm. Because of its good segmentation performance has become the focus of attention of many researchers. Super pixel algorithm divides the image into small regions according to the similarity of the features between pixels, which reduces the redundant information in the image. Image segmentation algorithm based on graph theory can transform image segmentation problem into network image segmentation problem by mapping image characteristics to graph theory characteristics. By combining global image segmentation with local information processing, the error caused by 'image discretization' can be reduced, and good segmentation results can be obtained. The image segmentation method based on semi-supervised learning can use a lot of unlabeled data to enhance the segmentation results. This paper focuses on the segmentation of intracranial hemorrhage region and the characteristics of super-pixel. This paper focuses on the application of super-pixel based image cutting algorithm and super-pixel based Tri-training algorithm in the field of intracranial hemorrhage region segmentation. The main work and contributions of this paper are as follows: 1. The common algorithms in the field of image segmentation and their application fields are studied. The characteristics of medical image segmentation, the pathogenesis of intracranial hemorrhage (ICH) and the characteristics of CT imaging are briefly introduced. 2. The classification, basic principle, advantages and disadvantages of the super-pixel algorithm are introduced in detail. Based on the existing super-pixel algorithm and the application scene of intracranial hemorrhage region segmentation, a new hypervoxel algorithm is proposed. 3. The basic principle, advantages and disadvantages of graph cutting algorithm are introduced in detail, aiming at the problems of artificial participation and insufficient model estimation in image segmentation algorithm based on graph cutting. In this paper, a supervised graph cutting algorithm based on Gao Si mixed model (GMMM) is proposed. According to the characteristics of medical images, the algorithm uses the existing tagged data as the prior knowledge. The model of foreground and background is established by GMM algorithm, so that the image segmentation algorithm based on graph cutting can realize automatic segmentation. 4. The basic principle, advantages and disadvantages of Tri-training algorithm are introduced in detail. According to the characteristics of hypervoxel, it is difficult to obtain tag samples in medical image segmentation. A region segmentation algorithm for intracranial hemorrhage based on hypervoxel and Tri-training algorithm is proposed, which utilizes a small amount of labeled data and a large amount of untagged data in the field of medical image processing. The automatic segmentation of medical image is realized.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R743.34;TP391.41

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