本文選題：醫(yī)學(xué)圖像 + 宮頸細(xì)胞學(xué)涂片　； 參考：《重慶大學(xué)》2014年博士論文

【摘要】：宮頸癌是嚴(yán)重威脅女性健康的惡性腫瘤之一，是發(fā)病率最高的三種癌癥之一。據(jù)GLOBOCAN2012報告，全球2012年新增宮頸癌病例52.7萬，約有26.5萬婦女死于宮頸癌。中國2012年新增宮頸癌病例達(dá)6.2萬，死亡病例達(dá)2.9萬。宮頸癌有較長的病變期，，如果能在早期發(fā)現(xiàn)并及時治療，治愈率較高，因而宮頸癌篩查對女性健康非常重要。 基于宮頸細(xì)胞學(xué)涂片的檢查技術(shù)是目前醫(yī)學(xué)上普遍采用的篩查技術(shù)，能夠有效發(fā)現(xiàn)宮頸癌前病變和早期宮頸癌。然而基于人工判讀的傳統(tǒng)宮頸細(xì)胞學(xué)篩查方式存在工作量大、成本高、可靠性與準(zhǔn)確性受到醫(yī)師專業(yè)技術(shù)和主觀情緒的影響等問題，因此開發(fā)基于計算機(jī)技術(shù)和圖像處理技術(shù)的宮頸細(xì)胞學(xué)涂片自動判讀系統(tǒng)對于宮頸癌的防治有著重要意義。 論文以宮頸細(xì)胞學(xué)涂片判讀自動化為研究目標(biāo)，將模擬細(xì)胞學(xué)醫(yī)師判讀宮頸細(xì)胞學(xué)涂片的方法作為自動判讀的研究思路，結(jié)合宮頸細(xì)胞病理學(xué)知識，運用圖像處理、本體建模、語義推理等技術(shù)，研究宮頸細(xì)胞學(xué)涂片自動判讀的部分關(guān)鍵技術(shù)。論文的主要內(nèi)容包括：宮頸細(xì)胞學(xué)涂片圖像的粗分割、重疊細(xì)胞分割、單細(xì)胞精確分割；細(xì)胞圖像的輪廓特征、染色質(zhì)特征提取；細(xì)胞的圖像特征、細(xì)胞學(xué)特征的本體建模與語義映射；宮頸細(xì)胞學(xué)涂片語義推理判讀模型和相關(guān)判讀規(guī)則構(gòu)建。論文突破以分類器作為判讀工具的傳統(tǒng)方法，提出基于語義推理的宮頸細(xì)胞學(xué)涂片判讀方法，為實現(xiàn)宮頸細(xì)胞學(xué)涂片判讀自動化提出了一種新途徑。論文的主要工作體現(xiàn)在以下5個方面： ①提出基于曲率的重疊細(xì)胞輪廓分離點檢測方法和基于橢圓曲線擬合的重疊細(xì)胞分割方法。前者首先通過分析輪廓曲線曲率的正負(fù)值檢測到凹區(qū)；然后根據(jù)曲率大小篩選出候選的重疊凹區(qū)，根據(jù)凹區(qū)寬度和凹區(qū)間距判定細(xì)胞重疊凹區(qū)；最后通過查找重疊凹區(qū)的曲率極點得到最終的細(xì)胞重疊接觸點（即分離點）。后者首先將相鄰分離點間的輪廓曲線上的點作為擬合數(shù)據(jù)，應(yīng)用基于最小二乘法的橢圓擬合方法得到擬合橢圓，再過濾掉面積過大和過小的擬合橢圓，得到用以提取分離線的擬合橢圓；然后提取擬合橢圓上分離點間的弧線段作為分離線，據(jù)此分離重疊的細(xì)胞；最后分析細(xì)胞面積與細(xì)胞重疊區(qū)域面積的關(guān)系以確定分離是否有效。該方法能保持細(xì)胞的原有形態(tài)，同時降低了欠分離和過分離的概率。 ②提出基于極坐標(biāo)系的梯度矢量流活動輪廓模型(PGVF Snake)。PGVFSnake首先把經(jīng)過預(yù)處理的細(xì)胞圖像從笛卡爾坐標(biāo)系變換到極坐標(biāo)系，計算基于極坐標(biāo)系的邊緣圖；然后提出“浪沙抑制”算法優(yōu)化PGVF模型中的邊緣圖，以消除細(xì)胞內(nèi)部雜質(zhì)對邊緣圖的干擾；最后使用邊緣圖作為活動輪廓模型中的外力，控制活動輪廓演化并收斂到細(xì)胞的真實邊緣。與RGVF活動輪廓模型的對比實驗結(jié)果表明，論文提出的方法在保證分割準(zhǔn)確度的前提下，分割速度提高了五倍以上。 ③提出基于線性幾何熱流演化的細(xì)胞輪廓不規(guī)則度特征提取方法。該方法首先把細(xì)胞輪廓曲線進(jìn)行幾何熱流演化，直到細(xì)胞輪廓演化為完全凸性為止，以此輪廓作為度量細(xì)胞輪廓不規(guī)則度的參考；然后比較演化后的輪廓曲線和原始輪廓曲線，提出不重疊區(qū)域總面積比、不重疊區(qū)域平均面積比等細(xì)胞輪廓不規(guī)則度特征描述子。在Herlev宮頸細(xì)胞圖像數(shù)據(jù)集上的實驗結(jié)果表明，論文所提方法提取的不規(guī)則度特征與宮頸細(xì)胞病變有明顯的相關(guān)性。 ④提出基于數(shù)學(xué)形態(tài)學(xué)的染色質(zhì)顆粒特征提取方法。該方法首先使用受限自適應(yīng)直方圖均衡方法增強(qiáng)細(xì)胞核圖像的對比度；然后使用不同尺度的形態(tài)學(xué)基本結(jié)構(gòu)元素進(jìn)行開運算，計算累積尺度分布，通過對累積尺度分布求導(dǎo)，得到染色質(zhì)顆粒尺度分布，并將顆粒尺度分布最大值所對應(yīng)的尺度作為染色質(zhì)顆粒特征描述子。在Herlev宮頸細(xì)胞圖像數(shù)據(jù)集上的實驗結(jié)果表明，論文所提方法提取的細(xì)胞染色質(zhì)顆粒特征與宮頸細(xì)胞病變有明顯的相關(guān)性。 ⑤基于本體與語義邏輯推理的宮頸細(xì)胞學(xué)涂片自動判讀方法。該方法模擬細(xì)胞學(xué)醫(yī)師判讀宮頸細(xì)胞學(xué)涂片的過程，結(jié)合圖像處理、知識表示、邏輯推理的相關(guān)理論，構(gòu)建宮頸細(xì)胞學(xué)涂片推理判讀模型。該方法首先建立與判讀相關(guān)的細(xì)胞本體、圖像特征本體、細(xì)胞學(xué)特征本體；然后提出基于語義規(guī)則的圖像特征到語義特征的映射方法；最后建立基于本體與語義邏輯推理的宮頸細(xì)胞學(xué)涂片自動判讀系統(tǒng)模型，并闡述了模型的原理。在Herlev宮頸細(xì)胞圖像數(shù)據(jù)集上與k最鄰近(kNN)分類器、支持向量機(jī)(SVM)的對比實驗結(jié)果表明，論文提出的宮頸細(xì)胞學(xué)涂片判讀方法對宮頸細(xì)胞病變有較高的靈敏度和特異度。
[Abstract]:Cervical cancer is one of the most malignant cancers that threaten women ' s health . It is one of the three cancers with the highest incidence . According to the report of GLOBOCAN2012 , the number of new cervical cancer increased by 52 . 7 million in 2012 , and about 265,000 women died of cervical cancer . In 2012 , the number of cervical cancer increased to 62,000 , with a mortality rate of 29,000 . Cervical cancer has a long lesion period . If it can be found in the early stage and treated in time , the cure rate is high , so cervical cancer screening is very important for women ' s health .

The examination technique based on cervical cytology smear is a commonly used screening technique in medicine , which can effectively detect cervical precancerous lesions and early cervical cancer . However , the traditional cervical cytology screening method based on manual interpretation has the problems of large workload , high cost , reliability and accuracy influenced by the professional technique and subjective emotion of the physician , etc . Therefore , it is important to develop cervical cytology smear automatic interpretation system based on computer technology and image processing technology for the prevention and treatment of cervical cancer .

Based on the study of cervical cytology smear , this paper studies the method of cervical cytology smear as the research thinking of automatic interpretation , combines the knowledge of cervical cell pathology , uses image processing , ontology modeling , semantic reasoning , and so on . The main contents of this paper include : coarse segmentation of smear image of cervical cytology , overlapping cell division , single cell precise segmentation ;
the contour feature of the cell image and the feature extraction of the chromatin ;
Image features , ontology modeling and semantic mapping of cellular features ;
Based on the traditional method of the classifier as the interpretation tool , this paper proposes a new method of cervical cytology smear based on semantic reasoning , which provides a new approach for the realization of cervical cytology smear automation . The main work of this paper is as follows :

( 1 ) the method of detecting the contour separation point of overlapped cells based on curvature and the overlapping cell segmentation method based on the elliptic curve fitting are proposed , the former firstly detects the concave area by analyzing the positive and negative values of the curvature of the contour curve ;
then selecting a candidate overlapping concave area according to the curvature size , and judging the cell overlapping concave area according to the concave area width and the concave area pitch ;
finally , obtaining the final cell overlapping contact point ( i.e . , the separation point ) by finding the pole of curvature of the overlapped concave area , and firstly , using the point on the contour curve between adjacent separation points as the fitting data , and applying the ellipse fitting method based on the least square method to obtain a fitting ellipse , and then filtering out the fitting ellipse with too large area and too small area to obtain a fitting ellipse for extracting the separation line ;
and then extracting the arc segment between the separated points on the fitting ellipse as a separation line , and separating the overlapping cells according to the separation line ;
Finally , the relationship between cell area and area of cell overlap is analyzed to determine whether the separation is effective . The method can preserve the original form of the cell , and reduce the probability of under - separation and over - separation .

Secondly , a gradient vector flow contour model ( PGVF Snake ) based on polar coordinate system is proposed . The PGVFSnake firstly transforms the preprocessed cell image from a Cartesian coordinate system to a polar coordinate system , and calculates an edge map based on a polar coordinate system ;
Then , an edge map in PGVF model is optimized by the " wave - sand suppression " algorithm to eliminate the interference of intra - cell impurities on the edge map ;
Finally , an edge map is used as the external force in the active contour model to control the evolution of the active contour and converge to the real edge of the cell . Compared with the RGVF active contour model , the results show that the proposed method improves the segmentation accuracy by more than five times under the premise of guaranteeing the segmentation accuracy .

( 3 ) The method of feature extraction of cell profile irregularity based on linear geometry heat flow evolution is proposed . The method includes the following steps : firstly , geometric thermal flow evolution is carried out on the contour curve of the cell until the evolution of the cell contour is complete convexity , and the contour is used as a reference for measuring the irregular degree of the contour of the cell ;
Then , the contour curve and the original contour curve after evolution are compared , and the feature descriptors of the area ratio of the non - overlapping area and the average area ratio of the non - overlapping area are presented . The experimental results on the Herlev cervical cell image data set show that the irregular characteristic extracted by the method is obviously related to the cervical cell lesion .

The method of feature extraction of chromatin particles based on mathematical morphology is proposed . Firstly , the contrast of nuclear image is enhanced by using constrained adaptive histogram equalization method .
Then using the basic structural elements of different scales to carry out the operation , the cumulative scale distribution is calculated , the scale distribution of the chromatin particles is obtained through the derivation of the cumulative scale distribution , and the scale corresponding to the maximum value of the particle size distribution is used as the characteristic description of the chromatin particles .

The invention relates to a cervical cytology smear automatic interpretation method based on ontology and semantic logic reasoning , which simulates the process of cervical cytology smear by a cytologic physician , combines image processing , knowledge representation and logical reasoning , and constructs a cervical cytology smear - based reasoning interpretation model .
then a mapping method based on semantic rules for image features to semantic features is proposed ;
Finally , a model of cervical cytology smear automatic interpretation system based on ontology and semantic logic reasoning is established , and the principle of the model is described . Compared with k nearest neighbor ( kNN ) classifier and support vector machine ( SVM ) on Herlev cervical cell image data set , the results show that cervical cytology smear interpretation method has high sensitivity and specificity for cervical cell pathological changes .

【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：R737.33;TP391.41

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本文編號：1804063