基于多圖譜的多發(fā)性硬化病變分割方法研究與應用
發(fā)布時間：2018-07-20 09:51
【摘要】：多發(fā)性硬化病變是一種比較常見的中樞神經(jīng)系統(tǒng)疾病,早期癥狀表現(xiàn)為四肢麻木乏力,后期會導致中風、認知障礙和視力退化等,嚴重威脅人類的健康。臨床上通過核磁共振圖像手動勾畫病變組織確診病情,費時費力且具有主觀不確定性。因此,研究多發(fā)性硬化病變的自動分割算法,提高其分割準確性和穩(wěn)定性,對于診斷、治療該疾病具有重要意義。在閱讀大量文獻的基礎(chǔ)之上,本文使用了一種基于多圖譜的分割方法,應用于多發(fā)性硬化病變的自動分割。研究工作主要有:(1)緒論。介紹了多發(fā)性硬化病變分割的研究意義,通過閱讀文獻,分析了現(xiàn)階段國內(nèi)外關(guān)于多發(fā)性硬化病變分割的研究現(xiàn)狀,對本文各部分研究內(nèi)容作了簡要說明。(2)核磁共振圖像的分割方法。介紹了腦部病變分割的必備理論知識,主要包括腦部醫(yī)學圖像相關(guān)知識和核磁共振成像技術(shù)以及核磁共振圖像分割方法。對閾值法、聚類法、分水嶺算法和小波變換進行了簡單介紹。基于圖譜的分割方法有三種:基于單圖譜的分割方法、基于平均圖譜的分割方法和基于多圖譜的分割方法。(3)基于多圖譜的多發(fā)性硬化病變分割算法。對基于多圖譜的分割方法流程進行了介紹,并對圖像預處理、圖像配準與分割以及圖像融合這三個步驟進行了詳細的介紹。在預處理過程中首先通過BET算法將原始圖像去腦殼,然后利用曲線擬合和遺傳算法進行偏移場校正。在配準方面,本文主要針對過去配準方法存在精度不高等問題,采用了fsl-anat配準方法。Fsl-anat配準方法利用局部相似性作為配準的相似性測度,并且對變形場施加了有效的約束及平滑可以達到比較好的配準效果。我們以fsl-anat配準的局部相似性作為融合時的權(quán)重,它適合于單模態(tài)和多模態(tài)配準任務。在融合過程中使用了加權(quán)選擇融合策略。(4)基于多圖譜的多發(fā)性硬化病變分割方法實驗結(jié)果分析。利用本文的算法進行實驗并實現(xiàn)多發(fā)性硬化病變分割。根據(jù)腦部病變組織的特征,本文選取了相似性測度來對分割結(jié)果進行評價。融合所得到的腦部病變組織在形狀和大小方面都和專家手動分割的結(jié)果比較接近,十組實驗的相似性測度值都達到了0.80以上。說明基于多圖譜的多發(fā)性硬化病變分割方法,可以高精度地分割出多發(fā)性硬化病變組織。
[Abstract]:Multiple sclerosis (MS) is a common disease of central nervous system (CNS). Its early symptoms are numbness and weakness of limbs, which can lead to stroke, cognitive impairment and visual degeneration, which seriously threaten human health. It is time-consuming and laborious and subjective uncertainty to diagnose the disease by manually delineating the pathological tissue by MRI image in clinic. Therefore, it is important for diagnosis and treatment of multiple sclerosis disease to study automatic segmentation algorithm to improve its segmentation accuracy and stability. On the basis of reading a large number of literatures, this paper uses a multi-atlas based segmentation method, which is applied to the automatic segmentation of multiple sclerosis lesions. The main research work is as follows: (1) introduction. This paper introduces the significance of the segmentation of multiple sclerosis, and analyzes the present situation of the segmentation of multiple sclerosis at home and abroad by reading the literature. The research contents of this paper are briefly described. (2) Segmentation method of nuclear magnetic resonance image. This paper introduces the necessary theoretical knowledge of brain lesion segmentation, including related knowledge of brain medical image, magnetic resonance imaging technology and nuclear magnetic resonance image segmentation method. The threshold method, clustering method, watershed algorithm and wavelet transform are briefly introduced. There are three segmentation methods based on map: one based on single map, one based on average spectrum and one based on multi-atlas. (3) multiple sclerosis segmentation algorithm based on multi-atlas. The segmentation process based on multi-atlas is introduced, and the three steps of image preprocessing, image registration and segmentation, and image fusion are introduced in detail. In the process of preprocessing, the original image is removed from the skull by BET algorithm, and then the offset field is corrected by curve fitting and genetic algorithm. In the aspect of registration, aiming at the problem of low precision in the past registration methods, this paper uses the fsl-anat registration method. Fsl-anat registration method uses local similarity as the similarity measure of registration. And the deformation field is subject to effective constraints and smoothing can achieve a better registration effect. We use the local similarity of fsl-anat registration as the weight of fusion, which is suitable for single and multimodal registration tasks. The weighted selection fusion strategy is used in the fusion process. (4) the experimental results of multiple sclerosis segmentation method based on multi-atlas are analyzed. The algorithm of this paper is used to carry out experiments and realize the segmentation of multiple sclerosis disease. According to the characteristics of brain lesions, the similarity measure is selected to evaluate the segmentation results. The shape and size of the brain lesions obtained by the fusion are close to the results of manual segmentation by experts. The similarity measures of the ten groups of experiments are above 0.80. The results show that multiple sclerosis segmentation method based on multi-atlas can be used to segment multiple sclerosis lesions with high accuracy.
【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】：R744.51;TP391.41
本文編號：2133154
[Abstract]:Multiple sclerosis (MS) is a common disease of central nervous system (CNS). Its early symptoms are numbness and weakness of limbs, which can lead to stroke, cognitive impairment and visual degeneration, which seriously threaten human health. It is time-consuming and laborious and subjective uncertainty to diagnose the disease by manually delineating the pathological tissue by MRI image in clinic. Therefore, it is important for diagnosis and treatment of multiple sclerosis disease to study automatic segmentation algorithm to improve its segmentation accuracy and stability. On the basis of reading a large number of literatures, this paper uses a multi-atlas based segmentation method, which is applied to the automatic segmentation of multiple sclerosis lesions. The main research work is as follows: (1) introduction. This paper introduces the significance of the segmentation of multiple sclerosis, and analyzes the present situation of the segmentation of multiple sclerosis at home and abroad by reading the literature. The research contents of this paper are briefly described. (2) Segmentation method of nuclear magnetic resonance image. This paper introduces the necessary theoretical knowledge of brain lesion segmentation, including related knowledge of brain medical image, magnetic resonance imaging technology and nuclear magnetic resonance image segmentation method. The threshold method, clustering method, watershed algorithm and wavelet transform are briefly introduced. There are three segmentation methods based on map: one based on single map, one based on average spectrum and one based on multi-atlas. (3) multiple sclerosis segmentation algorithm based on multi-atlas. The segmentation process based on multi-atlas is introduced, and the three steps of image preprocessing, image registration and segmentation, and image fusion are introduced in detail. In the process of preprocessing, the original image is removed from the skull by BET algorithm, and then the offset field is corrected by curve fitting and genetic algorithm. In the aspect of registration, aiming at the problem of low precision in the past registration methods, this paper uses the fsl-anat registration method. Fsl-anat registration method uses local similarity as the similarity measure of registration. And the deformation field is subject to effective constraints and smoothing can achieve a better registration effect. We use the local similarity of fsl-anat registration as the weight of fusion, which is suitable for single and multimodal registration tasks. The weighted selection fusion strategy is used in the fusion process. (4) the experimental results of multiple sclerosis segmentation method based on multi-atlas are analyzed. The algorithm of this paper is used to carry out experiments and realize the segmentation of multiple sclerosis disease. According to the characteristics of brain lesions, the similarity measure is selected to evaluate the segmentation results. The shape and size of the brain lesions obtained by the fusion are close to the results of manual segmentation by experts. The similarity measures of the ten groups of experiments are above 0.80. The results show that multiple sclerosis segmentation method based on multi-atlas can be used to segment multiple sclerosis lesions with high accuracy.
【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】：R744.51;TP391.41
【參考文獻】
相關(guān)期刊論文 前6條
1 石祥濱;房雪鍵;張德園;郭忠強;;基于深度學習混合模型遷移學習的圖像分類[J];系統(tǒng)仿真學報;2016年01期
2 吳丹;伍家松;曾瑞;姜龍玉;Lotfi Senhadji;舒華忠;;面向圖像分類的核主成分分析網(wǎng)絡（英文）[J];Journal of Southeast University(English Edition);2015年04期
3 王博;郭繼昌;張艷;;基于深度網(wǎng)絡的可學習感受野算法在圖像分類中的應用[J];控制理論與應用;2015年08期
4 楊昌俊;楊新;;基于圖割與快速水平集的腹部CT圖像分割[J];CT理論與應用研究;2011年03期
5 王春興;萬文博;馮超;宋秀梅;;基于小波正交變換的圖像數(shù)字信息隱寫算法[J];信息技術(shù)與信息化;2009年02期
6 魯向;蘆勤;羅述謙;;基于能量驅(qū)動的分水嶺算法在MRI海馬圖像分割中的應用[J];計算機輔助設(shè)計與圖形學學報;2008年06期
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