本文選題：基底膜分割 + 塊匹配��； 參考：《南方醫(yī)科大學(xué)》2017年碩士論文

【摘要】：慢性腎臟病已成為威脅全球公共健康的重要疾病,腎穿刺病理活檢是診斷慢性腎臟疾病的重要手段,借助透射電鏡(transmission electron microscopy,TEM)能觀察到腎小球細(xì)胞亞顯微結(jié)構(gòu)的病理改變,從而做出進(jìn)一步的病理診斷。研究指出,在腎小球細(xì)胞的亞顯微結(jié)構(gòu)中,腎小球基底膜(glomerular basement membrane,GBM)的變化與慢性腎臟疾病有密切的關(guān)系,如薄基底膜病表現(xiàn)為腎小球基底膜彌漫性變薄。因此在病理診斷過(guò)程中,醫(yī)生常常需要對(duì)基底膜進(jìn)行識(shí)別和測(cè)量。但是GBM的TEM灰度圖像紋理復(fù)雜,病變種類(lèi)繁多,且大部分基底膜與周?chē)M織結(jié)構(gòu)對(duì)比度較低,依靠肉眼進(jìn)行識(shí)別與測(cè)量不僅困難而且耗時(shí)。因此,利用計(jì)算機(jī)圖像處理技術(shù)對(duì)GBM區(qū)域進(jìn)行分割,能更快速直觀地觀察基底膜的形態(tài),有利于輔助慢性腎臟病的病理診斷。多年來(lái),圖像分割算法的研究一直是圖像處理領(lǐng)域的研究熱點(diǎn),國(guó)內(nèi)外提出的分割算法也很多,然而針對(duì)腎小球基底膜分割算法的研究,是在20多年前才開(kāi)始逐漸發(fā)展起來(lái)的。這主要因?yàn)樯飯D像本身帶有的復(fù)雜性,樣品制備過(guò)程的多變性,圖像對(duì)比度差,結(jié)構(gòu)模糊等特點(diǎn)而大大增加了圖像分析的難度系數(shù)和復(fù)雜性,從而在一定程度上使得腎小球基底膜分割算法的發(fā)展受到了限制。現(xiàn)階段已提出的基底膜分割方法可歸納為半自動(dòng)分割和全自動(dòng)分割兩大類(lèi)。這些方法主要基于圖像的灰度特征、紋理特征、梯度特征等屬性分割圖像,在處理灰度均勻、形態(tài)變化不大的小段基膜時(shí)能得到較好的效果,但是當(dāng)待分割的基底膜具有與周?chē)M織結(jié)構(gòu)對(duì)比度較低、自身形狀差異性大等特點(diǎn)時(shí),分割性能不穩(wěn)定,主要原因是因?yàn)檫@些方法對(duì)分割對(duì)象形狀的描述涉及較少,也不能根據(jù)已有的分割信息動(dòng)態(tài)調(diào)整分割規(guī)則。因此,基底膜分割的自動(dòng)化程度和精細(xì)水平仍然需要進(jìn)一步提升。針對(duì)現(xiàn)有方法存在的問(wèn)題和基于分割基底膜的需求,本文提出了兩種方法來(lái)完成基底膜的自動(dòng)分割。方法一是基于塊匹配算法的腎小球基底膜自動(dòng)分割。圖像塊匹配算法可以有效搜索圖像間的相似圖像塊,但是由于基底膜對(duì)比度低、自身形狀差異性大的特點(diǎn),僅僅從一幅參考圖像中搜索查詢圖像的相似塊將難以得到最優(yōu)的匹配結(jié)果。而且,當(dāng)參考圖像數(shù)量較多時(shí),逐一將查詢圖像與參考圖像進(jìn)行塊匹配,效率是很低的。因此,本文首先針對(duì)腎小球基底膜的特點(diǎn),,將塊匹配算法的搜索范圍從一幅參考圖像擴(kuò)展到多幅參考圖像,并采用了一種改進(jìn)的搜索方式提高匹配效率。然后開(kāi)始搜索最優(yōu)的圖像匹配塊,最后提取最優(yōu)匹配塊對(duì)應(yīng)的標(biāo)記匹配塊進(jìn)行加權(quán),重組為腎小球基底膜的初始分割結(jié)果。對(duì)于匹配結(jié)果出現(xiàn)的假陽(yáng)性問(wèn)題,本文采用數(shù)學(xué)形態(tài)學(xué)的方法對(duì)分割結(jié)果進(jìn)行后處理,得到精度更高的結(jié)果。方法二是基于隨機(jī)森林分類(lèi)器的腎小球基底膜的自動(dòng)分割。隨機(jī)森林算法通過(guò)bootstrap抽樣技術(shù),產(chǎn)生新的訓(xùn)練樣本集合,然后對(duì)每個(gè)bootstrap樣本進(jìn)行決策樹(shù)建模,生成由k個(gè)決策樹(shù)組合成的隨機(jī)森林,最后通過(guò)投票的方式,對(duì)新數(shù)據(jù)的分類(lèi)結(jié)果進(jìn)行預(yù)測(cè)。研究表明,隨機(jī)森林算法具有較高的預(yù)測(cè)準(zhǔn)確率,對(duì)異常值和噪聲具有很好的容忍度。但是由于腎小球TEM圖像中,不同圖像之間存在灰度差異大的問(wèn)題,使得采用隨機(jī)森林分類(lèi)處理海量數(shù)據(jù)時(shí),會(huì)導(dǎo)致部分像素點(diǎn)的分類(lèi)混亂,致使基底膜的分割準(zhǔn)確率不高。本文在隨機(jī)森林的基礎(chǔ)上,引入多重隨機(jī)森林的概念,從使用一個(gè)隨機(jī)森林進(jìn)行分類(lèi)擴(kuò)展到使用多個(gè)隨機(jī)森林分類(lèi),使得當(dāng)森林?jǐn)?shù)量足夠大時(shí),總有一張或多張訓(xùn)練圖像的灰度跟待分割圖像的灰度接近,進(jìn)而克服不同圖像之間灰度差異帶來(lái)的分割準(zhǔn)確率不高的問(wèn)題,提高腎小球基底膜的分割準(zhǔn)確率。在采集到的500組腎小球透射電鏡圖像上進(jìn)行測(cè)試,方法一得到的Jaccard系數(shù)最低為83%,最高為95%;方法二得到的最低為84.6%,最高為92%。實(shí)驗(yàn)結(jié)果表明,本文提出的兩種圖像自動(dòng)分割方法,在腎小球基底膜的自動(dòng)分割上取得了精度較高的分割結(jié)果,可以為腎活檢病理診斷提供有價(jià)值的信息。
[Abstract]:Chronic renal disease has become an important disease that threatens the global public health. Renal biopsy is an important means for the diagnosis of chronic renal diseases. The pathological changes of the submicroscopic structure of glomerular cells can be observed by transmission electron microscopy (TEM), and further pathological diagnosis is made. The changes in the glomerular basement membrane (GBM) in the submicroscopic structure of the glomeruli are closely related to the chronic renal disease, such as the thin basement membrane disease showing the diffuse thinning of the glomerular basement membrane. Therefore, the doctors often need to identify and measure the basement membrane during the pathological diagnosis. But the TEM ash of GBM It is difficult and time-consuming to recognize and measure the GBM region by the computer image processing technology, so it can be more quickly and intuitively observed the morphology of the basement membrane, which is beneficial to auxiliary chronic kidney disease. The research of image segmentation algorithm has been a hot topic in the field of image processing for many years. There are also many segmentation algorithms at home and abroad. However, the research on the segmentation algorithm for glomerular basement membrane has been developed more than 20 years ago. This is mainly due to the complexity of the biological image itself and the sample system. The variability of the preparation process, the poor image contrast and the fuzzy structure greatly increase the difficulty and complexity of the image analysis, so that the development of the glomerular basement membrane segmentation algorithm is limited to a certain extent. The proposed method of basement membrane segmentation can be divided into two main parts: semi-automatic segmentation and full automatic segmentation at present. These methods are mainly based on the image's gray features, texture features, gradient features and other attributes to segment the image. It can get better results in the small segment base membrane when the gray level is uniform and the shape is not changed. However, when the base film to be divided has the characteristics of low contrast to the surrounding structure and large difference in its shape, the segmentation property is divided. The main reason for the instability is that these methods have less description of the shape of the segmented objects, and can not dynamically adjust the segmentation rules according to the existing segmentation information. Therefore, the automation and fine level of the basement membrane segmentation still needs to be further improved. In this paper, two methods are proposed to automatically segment the basement membrane. One is the automatic segmentation of the glomerular basement membrane based on block matching algorithm. The image block matching algorithm can effectively search the similar image blocks between the images. But because of the low contrast of the basement membrane and the large difference of the shape of the image, the image block matching algorithm is only searched from a reference image. It is difficult to get the optimal matching result for the similar block of the cable query image. Moreover, when the number of reference images is large, the efficiency is very low when the query image is matched with the reference image one by one. Therefore, this paper first extends the search range from a reference image to a number of references for the characteristics of the glomerular basement membrane. The image is tested and an improved search method is used to improve the matching efficiency. Then the optimal image matching block is searched. Finally, the marker matching block corresponding to the optimal matching block is weighted to restructure the initial segmentation result of the glomerular basement membrane. The mathematical morphology is used in this paper for the false positive problem of the matching results. Methods the result of the segmentation is processed and the result of higher precision is obtained. Method two is based on the automatic segmentation of the glomerular basement membrane based on the random forest classifier. The random forest algorithm produces a new set of training samples by bootstrap sampling, and then models each bootstrap sample to make the combination of K decision tree. The random forest, finally, predicts the results of the new data by voting. The study shows that the random forest algorithm has a high prediction accuracy and has a good tolerance to the outliers and noise. But because of the large scale difference between the different images in the glomerular TEM image, the random forest is used in the random forest. On the basis of the random forest, this paper introduces the concept of multiple random forests on the basis of random forests, and extends from a random forest to the use of multiple random forest classifications to make the total number of forests large enough when the number of forests is large enough. The gray level of one or more training images is close to the gray level of the image to be divided, and then it overcomes the problem of low segmentation accuracy caused by the difference of gray level between different images, and improves the segmentation accuracy of the glomerular basement membrane. In the 500 groups of collected glomerular transmission electron microscopy images, the method one obtains the lowest Jaccard coefficient. 83%, the highest is 95%, and the minimum of method two is 84.6%. The highest 92%. experiment results show that the two image automatic segmentation methods proposed in this paper have obtained high precision segmentation results in the automatic segmentation of the glomerular basement membrane, which can provide valuable information for pathological diagnosis of renal biopsy.

【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】：2017
【分類(lèi)號(hào)】：R692;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 佘廣南;陳瑩胤;鐘麗明;陽(yáng)維;馮前進(jìn);;基于密集特征匹配的胸片肺野自動(dòng)分割[J];南方醫(yī)科大學(xué)學(xué)報(bào);2016年01期

2 潘文波;田向輝;吳淋淋;劉樹(shù)軍;車(chē)彥海;;1600例腎穿刺活檢病理資料回顧性研究[J];中國(guó)實(shí)驗(yàn)診斷學(xué);2014年08期

3 叢日華;靳蕊霞;;69例膜性腎病臨床特點(diǎn)與病理資料分析[J];解放軍醫(yī)學(xué)院學(xué)報(bào);2014年10期

4 王素霞;柴立軍;謝燕玲;王書(shū)合;;腎組織內(nèi)特殊有形結(jié)構(gòu)形成的腎臟病的電鏡診斷[J];電子顯微學(xué)報(bào);2014年02期

5 潘曉霞;陳楠;;電鏡在遺傳性腎小球疾病診斷中的應(yīng)用價(jià)值[J];中國(guó)實(shí)用內(nèi)科雜志;2014年03期

6 詹曙;姚堯;高賀;;基于隨機(jī)森林的腦磁共振圖像分類(lèi)[J];電子測(cè)量與儀器學(xué)報(bào);2013年11期

7 尚瑜;尹愛(ài)萍;;5000例腎臟疾病患者腎組織活檢臨床病理資料分析[J];中國(guó)慢性病預(yù)防與控制;2011年03期

8 章友康;王素霞;;超微病理在腎臟疾病診斷中的作用及其評(píng)價(jià)[J];診斷學(xué)理論與實(shí)踐;2007年06期

9 王敏尤;秦斌杰;;基于圖像塊匹配和測(cè)地線活動(dòng)輪廓模型的腫瘤自動(dòng)檢測(cè)算法[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2007年06期

10 山海濤,郭建星,耿則勛;影像匹配中幾種相似性測(cè)度的分析[J];測(cè)繪信息與工程;2003年04期

相關(guān)碩士學(xué)位論文 前1條

1 李穆;腎小球TEM病理圖像的大視野拼接及基底膜分割[D];南方醫(yī)科大學(xué);2016年

，

本文編號(hào)：1893008