本文選題：腦腫瘤 + 圖像分割��； 參考：《南方醫(yī)科大學(xué)》2014年碩士論文

【摘要】：腦腫瘤是指發(fā)生于顱腔內(nèi)的神經(jīng)系統(tǒng)腫瘤,包括原發(fā)性腫瘤和繼發(fā)性腫瘤兩類。原發(fā)性腦腫瘤是指發(fā)生于顱內(nèi)腦組織、腦神經(jīng)、腦膜、垂體以及胚胎殘余組織等的腫瘤；繼發(fā)性腦腫瘤是指顱腔外身體其他部位的惡性腫瘤轉(zhuǎn)移或侵入顱內(nèi)形成的轉(zhuǎn)移瘤。在人群中,腦腫瘤發(fā)病率很高。據(jù)調(diào)查,原發(fā)性腦腫瘤的發(fā)病率為7.8/10萬人～12.5/10萬人。腦腫瘤可發(fā)生于任何年齡,以20～50歲年齡組多見。由于顱內(nèi)腫瘤發(fā)生于有限的顱腔容積內(nèi),無論良性還是惡性腫瘤,占位效應(yīng)本身就可以壓迫腦組織并造成功能損害,甚至威脅生命。 近年來,隨著環(huán)境污染的加劇、生活壓力的增大以及遺傳因素的影響,腦腫瘤的發(fā)病率呈逐年上升的趨勢(shì)。最新的腫瘤流行病學(xué)調(diào)查研究表明,腦腫瘤發(fā)病率約占全身腫瘤發(fā)病率的1.4%,死亡率超過2.4%。腦腫瘤是一類發(fā)病率、死亡率都很高的腫瘤疾病,已經(jīng)成為危害人類生命健康的殺手。 腦腫瘤的臨床表現(xiàn)多種多樣,早期癥狀有時(shí)不典型,甚至出現(xiàn)“例外”情況,而當(dāng)腦腫瘤的基本特征均己具備時(shí),病情往往已屬晚期。腦腫瘤的臨床表現(xiàn)一般為由顱內(nèi)壓增高引起的頭痛、惡性嘔吐、視乳頭水腫和視力障礙、癲癇等,以及定位癥狀與體征如肌肉力減退、癲癇等。 臨床上,醫(yī)生通過詳細(xì)的病史詢問和神經(jīng)系統(tǒng)檢查,可以了解起病方式、首發(fā)癥狀、癥狀經(jīng)過以及有無高顱壓和局灶性腦癥狀,根據(jù)這些可以推斷是否存在腦腫瘤,一般凡有進(jìn)行性顱內(nèi)壓增高并伴隨有局灶腦部癥狀者,基本可以確定腦腫瘤的存在。進(jìn)一步參照腦腫瘤的好發(fā)年齡、好發(fā)部位、癥狀的發(fā)生方式及進(jìn)展情況以判斷腫瘤的部位和性質(zhì)。醫(yī)生在診斷腦腫瘤時(shí)常輔助使用影像診斷來進(jìn)行。計(jì)算機(jī)斷層攝影(Computed Tomography, CT)和磁共振成像(Magnetic Resonance Imaging, MRI)己成為目前診斷腦腫瘤的最主要影像學(xué)手段。CT和MRI的應(yīng)用大大提高的腦腫瘤的診斷能力,也使得腦腫瘤的臨床治療效果得到了改善。 早期診斷,早期治療是包括腦腫瘤在內(nèi)所有疾病的醫(yī)療原則,治療越早,效果越好。腦腫瘤的治療原則上是以手術(shù)治療為主,輔助以放療、化療、生物治療等方式。手術(shù)治療需要對(duì)腦腫瘤進(jìn)行合理、有效的檢測(cè)和監(jiān)測(cè),以盡可能的切除腦腫瘤。但由于惡性腫瘤的侵入性生長(zhǎng),使其在圖像上表現(xiàn)為與周圍組織邊界模糊,這給臨床醫(yī)生的診斷帶來了很大困難。同時(shí),醫(yī)生在診斷腦腫瘤時(shí),存在一定差異。不同醫(yī)生對(duì)同一個(gè)病人的腦腫瘤圖像,或是同一個(gè)醫(yī)生在不同時(shí)期對(duì)同一個(gè)病人的腦腫瘤圖像的分割結(jié)果存在一定的差異。利用計(jì)算機(jī)技術(shù)對(duì)腦腫瘤進(jìn)行有效分割,越來越受到人們的重視。 在現(xiàn)代醫(yī)學(xué)影像診斷技術(shù)中,MRI是一種重要的解剖性影像診斷技術(shù)。磁共振成像具有較高的組織對(duì)比度和組織分辨率,對(duì)組織的形態(tài)和病理改變有很高的敏感性,無電離輻射,屬于無損傷性檢查。同時(shí),它可以進(jìn)行多參數(shù)、多序列,任意方位的成像。目前,MRI已成為診斷腦腫瘤的主要手段。臨床上,腦腫瘤的分割一般由經(jīng)驗(yàn)豐富的醫(yī)生在MRI圖像上,利用計(jì)算機(jī)輔助軟件,手動(dòng)分割來完成的。手動(dòng)分割有很強(qiáng)的主觀性,可重復(fù)操作性差。同時(shí),在磁共振成像的過程中,由于噪聲、組織運(yùn)動(dòng)和局部體積效應(yīng)等的影響,獲得的圖像對(duì)比度低,不同病灶與周圍組織之間邊界模糊,這又給手動(dòng)分割帶來了更大的困難。因此,利用腦腫瘤分割算法對(duì)腦腫瘤進(jìn)行效應(yīng)分割,成為臨床上治療腦腫瘤的迫切需要。 圖像分割就是把圖像中的感興趣區(qū)域分出來,使這些分開的區(qū)域之間相互不交叉,每個(gè)區(qū)域都滿足特定區(qū)域的一致性。圖像分割是圖像分析和圖像理解的基礎(chǔ),在醫(yī)學(xué)、軍事等領(lǐng)域都有著廣泛的應(yīng)用,吸引了國內(nèi)外許多專家學(xué)者進(jìn)行研究。隨著研究的深入,研究人員提出了很多實(shí)用的分割算法,大致可分為基于區(qū)域的分割方法、基于邊緣的分割方法、結(jié)合特定理論工具的分割方法等幾類�；趨^(qū)域的分割方法主要包括閾值法、區(qū)域生長(zhǎng)和分裂合并法、特征聚類法以及基于馬爾科夫隨機(jī)場(chǎng)的方法�；谶吘壍姆指罘椒ㄊ峭ㄟ^檢測(cè)邊緣來進(jìn)行分割的。為此,設(shè)計(jì)成了各種檢測(cè)算子,如Sobel算子、LOG算子、Krish算子、Canny算子等。結(jié)合特定理論工具的分割方法主要包括基于數(shù)學(xué)形態(tài)學(xué)的分割方法、基于神經(jīng)網(wǎng)絡(luò)的分割方法、基于模糊理論的分割方法、基于分形理論的分割方法,基于形變模型的分割方法等。這些分割方法各有優(yōu)缺點(diǎn),基于馬爾科夫隨機(jī)場(chǎng)的分割方法雖然得到了廣泛應(yīng)用,但邊緣定位不準(zhǔn)確,運(yùn)算量大,而且優(yōu)化過程比較復(fù)雜�；谶吘墮z測(cè)的分割方法定位比較精確,但受噪聲影響大,僅使用該方法很難對(duì)醫(yī)學(xué)圖像進(jìn)行有效分割�；谏窠�(jīng)網(wǎng)絡(luò)的分割方法對(duì)隨機(jī)噪聲具有很強(qiáng)的魯棒性,對(duì)人工干預(yù)要求比較小,但是圖像的能量函數(shù)容易陷入局部最小�；谛巫兡Ｐ偷姆指罘椒▽�(duì)噪聲和偽邊界具有很強(qiáng)的魯棒性,并且可以直接產(chǎn)生閉合參數(shù)曲線或曲面,但它對(duì)輪廓的初始位置比較敏感。 隨著理論研究的深入,細(xì)胞自動(dòng)機(jī)越來越受到研究者的關(guān)注。細(xì)胞自動(dòng)機(jī)(Cellular Automata,簡(jiǎn)稱CA)是定義在有限狀態(tài)、離散的細(xì)胞空間上,并按照一定的局部規(guī)則,在離散的時(shí)間維度上進(jìn)行演化的動(dòng)力學(xué)系統(tǒng)。細(xì)胞自動(dòng)機(jī)主要包括細(xì)胞節(jié)點(diǎn)集合、鄰域系統(tǒng)以及狀態(tài)轉(zhuǎn)移函數(shù)。細(xì)胞自動(dòng)機(jī)的空間結(jié)構(gòu)與數(shù)字圖像的網(wǎng)格式存儲(chǔ)結(jié)構(gòu)具有一致性,可以把數(shù)字圖像中的每個(gè)像素點(diǎn)一一對(duì)應(yīng)到細(xì)胞自動(dòng)機(jī)空間中的每個(gè)細(xì)胞單位上。依據(jù)處理目的的不同來制定不同的狀態(tài)轉(zhuǎn)移函數(shù),從而得到不同的分割效果。 Grow Cut算法是一種基于細(xì)胞自動(dòng)機(jī)的分割算法,主要應(yīng)用于圖像編輯和醫(yī)學(xué)圖像處理領(lǐng)域。它是一種交互式分割方法,可以利用一些先驗(yàn)知識(shí)進(jìn)行簡(jiǎn)單的交互處理,簡(jiǎn)化分割過程。使用Grow Cut算法對(duì)MRI腦腫瘤圖像進(jìn)行分割時(shí),分割結(jié)果不理想,主要因?yàn)樵撍惴ǖ臓顟B(tài)轉(zhuǎn)移函數(shù)不適用于復(fù)雜的MRI腦腫瘤圖像,往往誤判腦腫瘤邊界附近的像素點(diǎn),不能準(zhǔn)確找到腦腫瘤邊界。 本文提出了一種新的基于細(xì)胞自動(dòng)機(jī)的MRI腦腫瘤分割算法。該分割算法在細(xì)胞自動(dòng)機(jī)的基礎(chǔ)上,引入了活動(dòng)輪廓模型來對(duì)分割算法進(jìn)行優(yōu)化。本文對(duì)種子點(diǎn)的選取進(jìn)行了改良,只需要手動(dòng)標(biāo)記前景種子點(diǎn)就可以了,背景種子點(diǎn)通過一定的計(jì)算來得到,這就簡(jiǎn)化了人工交互過程。使用8鄰域的Moore鄰域來定義每個(gè)像素點(diǎn)的鄰域空間,針對(duì)MRI腦腫瘤圖像的特點(diǎn),構(gòu)造合適的狀態(tài)轉(zhuǎn)移函數(shù),并使用一些先驗(yàn)知識(shí)對(duì)狀態(tài)轉(zhuǎn)移函數(shù)進(jìn)行約束。當(dāng)細(xì)胞自動(dòng)機(jī)演化結(jié)束后,可以得到腦腫瘤圖像的標(biāo)號(hào)圖,此時(shí)得到的分割結(jié)果還不夠精確,會(huì)把腦腫瘤區(qū)域附近的一些非腫瘤像素點(diǎn)錯(cuò)分為腫瘤像素點(diǎn)。使用活動(dòng)輪廓模型對(duì)標(biāo)號(hào)圖進(jìn)行優(yōu)化處理,最終可得到精確的腦腫瘤分割結(jié)果。 使用我們提出的分割算法對(duì)臨床腦腫瘤圖像進(jìn)行分割,實(shí)驗(yàn)數(shù)據(jù)是由在線數(shù)據(jù)庫BRATS2012提供的對(duì)比增強(qiáng)T1加權(quán)MRI圖像。把專家手動(dòng)分割的結(jié)果作為分割真值,來對(duì)分割結(jié)果進(jìn)行評(píng)價(jià)。使用Dice系數(shù)(DSC)、JM相似性系數(shù)(Jaccard's Measure)、Sensitivity (Sens.)和假陽性率(FPR)等技術(shù)評(píng)價(jià)指標(biāo)對(duì)分割結(jié)果進(jìn)行評(píng)價(jià)。評(píng)價(jià)結(jié)果顯示,本文提出的分割算法具有很高的準(zhǔn)確性,與真值結(jié)果很接近。由此驗(yàn)證了本文分割方法具有很高的可行性和實(shí)用性。
[Abstract]:Brain tumors refer to the tumors of the nervous system occurring in the cranial cavity, including two types of primary and secondary tumors. Primary brain tumors refer to tumors that occur in the brain, the brain, the meninges, the pituitary, and the remnant tissues of the embryo; secondary brain tumors refer to the metastasis or invasion of the other parts of the outer body of the skull. The incidence of brain tumors is high in the population. It is investigated that the incidence of primary brain tumors is 7.8/10 million to 12.5/10 million. Brain tumors can occur at any age and are more common in the age group of 20~50 years. It can oppress brain tissue and cause functional damage and even threaten life.
In recent years, with the intensification of environmental pollution, the increase of life pressure and the influence of genetic factors, the incidence of brain tumors is increasing year by year. The latest epidemiological investigation of tumor shows that the incidence of brain tumors is about 1.4% of the incidence of whole body tumors. The mortality rate over 2.4%. is a kind of incidence and the mortality is very high. Tumor diseases have become the killer of human life and health.
The clinical manifestations of brain tumors are varied. The early symptoms are sometimes untypical and even "exceptions". When the basic features of the brain tumors are all possessed, the condition is often late. The clinical manifestations of brain tumors are usually caused by increased intracranial pressure, emetic vomiting, papillatous papillae, visual impairment, epilepsy and so on. Symptoms and signs such as muscle degeneration, epilepsy, and so on.
Clinically, doctors can understand the mode of onset, first symptoms, symptoms, and whether there are high intracranial pressure and focal brain symptoms by detailed medical history inquiry and nervous system examination. According to these, it is possible to deduce whether there is brain tumor. In general, patients with progressive intracranial pressure and accompanied with focal brain symptoms can basically determine the brain swelling. The presence of the tumor. Further reference to the good onset age of the brain tumor, the location of the good hair, the way of the symptoms and the progress to determine the location and nature of the tumor. The doctor is often assisted by imaging diagnosis in the diagnosis of brain tumors. Computed tomography (Computed Tomography, CT) and magnetic resonance imaging (Magnetic Resonance Imaging, MRI) It has become the most important imaging method for the diagnosis of brain tumors,.CT and MRI, which have greatly improved the diagnostic ability of brain tumors, and have improved the clinical therapeutic effect of brain tumors.
Early diagnosis, early treatment is the medical principle of all diseases including brain tumors, the earlier the treatment, the better the effect. The treatment of brain tumors is mainly based on surgical treatment, assisted by radiotherapy, chemotherapy, biological treatment and so on. Surgical treatment requires rational, effective detection and monitoring of brain tumors, so as to remove brain tumors as much as possible. But because of the invasive growth of the malignant tumor, it shows the blurring of the boundary of the surrounding tissue on the image, which is very difficult for the diagnosis of the clinician. At the same time, there are certain differences between the doctors and the brain tumor in the diagnosis of the same patient. There are some differences in the segmentation results of human brain tumor images. More and more attention has been paid to the effective segmentation of brain tumors using computer technology.
In modern medical imaging diagnosis technology, MRI is an important anatomical imaging diagnosis technology. Magnetic resonance imaging has high tissue contrast and tissue resolution, has high sensitivity to the morphological and pathological changes of tissue, without ionizing radiation, and is a noninvasive examination. At the same time, it can carry on multi parameter, multi sequence and arbitrary side. Imaging. At present, MRI has become a major means of diagnosis of brain tumors. Clinical segmentation of brain tumors is usually done by experienced doctors on MRI images, using computer aided software and manual segmentation. Manual segmentation has strong subjectivity and poor repeatability. In the process of magnetic resonance imaging, noise, With the influence of tissue movement and local volume effect, the image contrast is low and the boundary between the different focus and the surrounding tissue is blurred. This brings more difficulty to the manual segmentation. Therefore, it is an urgent need to use the brain tumor segmentation algorithm to segment the brain tumor and to treat the brain tumor in clinical.
Image segmentation is to divide the region of interest in the image, which makes the separate areas not cross each other. Each region meets the consistency of the specific region. Image segmentation is the basis of image analysis and image understanding. It has a wide application in medical and military fields, which attracts many experts and scholars at home and abroad to study. With the deepening of the research, researchers have proposed many practical segmentation algorithms, which can be roughly divided into region based segmentation methods, edge based segmentation methods and the segmentation methods of specific theoretical tools. The region based segmentation methods mainly include threshold method, regional growth and split merge method, feature clustering method and base. In Markov random field, the edge based segmentation method is segmented by detecting edges. Therefore, various detection operators, such as Sobel operator, LOG operator, Krish operator and Canny operator, are designed. The segmentation method combined with specific theoretical tools mainly includes the segmentation method based on mathematical morphology, based on neural network. The segmentation method, the segmentation method based on the fuzzy theory, the segmentation method based on the fractal theory, the segmentation method based on the deformation model, and so on. These segmentation methods have the advantages and disadvantages. Although the segmentation method based on Markov random field has been widely used, the edge location is inaccurate, the computation is large and the optimization process is complex. The segmentation method of edge detection is more accurate, but it is greatly affected by noise. It is difficult to effectively segment medical images by using this method only. The segmentation method based on neural network has strong robustness to random noise and small requirement for artificial intervention, but the energy function of the image is easy to fall into the local minimum. The segmentation method of the model has strong robustness to the noise and the pseudo boundary, and can directly produce the closed parameter curve or surface, but it is more sensitive to the initial position of the contour.
Cellular automata (Cellular Automata) is a dynamic system defined in a finite state, discrete cellular space, and evolves in discrete time dimensions. Cellular automata mainly include cell nodes, which are defined in a finite state, discrete cellular space, and according to certain local rules. Set, neighborhood system and state transfer function. The spatial structure of cellular automata is consistent with the network format storage structure of digital images. Each pixel in the digital image can be corresponded to each cell unit in the cellular automaton space. Different state transfer functions are formulated according to the difference of the processing order. Different segmentation results are obtained.
Grow Cut algorithm is a kind of segmentation algorithm based on cellular automata, which is mainly used in image editing and medical image processing. It is an interactive segmentation method. It can use some prior knowledge to do simple interactive processing and simplify the segmentation process. Using Grow Cut algorithm to segment the image of MRI brain tumor, the segmentation results are not. Ideal, mainly because the state transfer function of the algorithm does not apply to the complex MRI brain tumor image, often misjudges the pixels near the brain tumor boundary, and can not accurately find the brain tumor boundary.
In this paper, a new MRI brain tumor segmentation algorithm based on cellular automata is proposed. Based on the cellular automata, the active contour model is introduced to optimize the segmentation algorithm. In this paper, the selection of seed points is improved, only the foreground seed points need to be labelled manually, and the background seed points have been passed through certain points. This simplifies the process of artificial interaction. We use the Moore neighborhood of the 8 neighborhood to define the neighborhood of each pixel, construct a suitable state transfer function for the features of the MRI brain tumor image, and use some prior knowledge to deal with the state transfer function. When the cellular automaton is over, the brain can be obtained. The segmentation results of the tumor image are not accurate enough, and some non tumor pixels near the brain tumor area will be misclassified as the tumor pixels. The active contour model is used to optimize the label map, and the accurate segmentation results of the brain tumor can be obtained.
We use the segmentation algorithm we proposed to segment the clinical brain tumor images. The experimental data is the contrast enhanced T1 weighted MRI image provided by the online database BRATS2012. The result of the segmentation is evaluated by the result of the expert manual segmentation as the segmentation true value. The Dice coefficient (DSC), the JM similarity coefficient (Jaccard's Measure) and Sensitivi are used. Ty (Sens.) and false positive rate (FPR) evaluation indexes are used to evaluate the segmentation results. The results show that the segmentation algorithm proposed in this paper is very accurate and close to the true value results. Thus, it is proved that the segmentation method is highly feasible and practical.
【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R739.41;R445.2

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