本文選題：宮頸細胞圖像　切入點：圖像分割　出處：《廣西師范大學》2016年碩士論文

【摘要】：本文以宮頸細胞圖像為例,對細胞圖像的分割、形態(tài)特征和極坐標特征、識別應用的技術(shù)進入了深入的研究,主要是包括宮頸細胞圖像的細胞質(zhì)、細胞核與背景的輪廓精確提取,宮頸細胞圖像多特征融合及細胞分類識別方法。主要研究從以下幾個部分：(1).提出了一種基于自適應閾值和射線梯度的GVF Snake主動輪廓模型,用來定位宮頸單細胞圖像的細胞核與細胞質(zhì)的邊緣。GVF Snake主動輪廓模型應用比較廣泛的主動輪廓模型的目標邊緣跟蹤算法,但是宮頸細胞圖像,特別是細胞質(zhì)邊緣相對模糊、細胞核與細胞質(zhì)的邊緣相互吸附難以分開,還有干擾性的血細胞及炎癥細胞、染色度分布不均勻,都是導致GVF Snake模型細胞邊緣吸附到錯誤的位置。為了解決以上難題,本文研究了以下方法：首先是利用自適應閾值去除細胞背景,然后使用射線梯度方向的信息計算細胞灰度值,最后根據(jù)射線上的灰度值使用GVF Snake模型演化,在演化過程中使用棧的灰度差補償算法,結(jié)合正向灰度差抑制能夠很好的克服噪聲、血細胞及炎癥細胞等虛假邊緣的信息影響。本文使用的Herlev數(shù)據(jù)庫驗證了該方法的有效性和可行性。(2).在對宮頸細胞圖像進行精確分割的基礎(chǔ)上,研究了宮頸細胞圖像的形態(tài)特征參數(shù),主要包括9種幾何特征和4種紋理特征。9種幾何特征分別為：細胞質(zhì)的周長、細胞核的周長、豎直方向的最長軸、水平方向的最寬軸、細胞核與細胞質(zhì)的比率、軸中心到周長的最長長度、軸中心到周長的平均長度、重心到周長的最長長度、重心到周長的平均長度；4種紋理特征：共生矩陣的熵、共生矩陣的對比度、對比度和粗糙度。宮頸單細胞圖像是由細胞核、細胞質(zhì)和背景三個區(qū)域都可以轉(zhuǎn)化到極坐標系下,提取極坐標下的極經(jīng)灰度值,360條極經(jīng)的灰度值組成一個特征矩陣。本文將極坐標下的特征向量與前面的形態(tài)特征進行融合,來研究宮頸細胞的識別。(3).使用基于AdaBoost與SVM算法結(jié)合的向量機,改善了分類器的穩(wěn)定性和差異性。采用的AdaBoost-SVM分類器將提取的宮頸細胞多特征進行融合,再識別分類應用。雙重分類器結(jié)合可以彌補單個分類器的缺點,提升分類識別效率。通過特征提取方法與AdaBoost-SVM多特征融合分類器結(jié)合,實驗結(jié)果證明：提高了宮頸細胞涂片篩查的效率和準確率,降低了宮頸癌的誤診率。本文對宮頸細胞圖像的分割、特征提取和宮頸細胞的分類識別等進行了系統(tǒng)性的研究和改進。實驗結(jié)果表明：本文的方法能較好的完成宮頸細胞的定量分析,對于宮頸細胞圖像的自動篩查分析系統(tǒng)具有較好的應用價值。
[Abstract]:In this paper, we take cervical cell image as an example, the segmentation of cell image, morphological features and polar coordinate features, recognition of the application of technology into in-depth research, mainly including the cervical cell image of the cytoplasm, The precise contour extraction of nucleus and background, the multi-feature fusion of cervical cell image and the method of cell classification recognition are studied. A GVF Snake active contour model based on adaptive threshold and ray gradient is proposed from the following parts: 1. GVF Snake active contour model used to locate the edge of nucleus and cytoplasm of single cell image of cervix is widely used in target edge tracking algorithm of active contour model, but the image of cervical cell, especially the edge of cytoplasm, is relatively fuzzy. It is difficult to separate the nuclear and cytoplasmic edges from each other, as well as interfering blood cells and inflammatory cells. The uneven distribution of staining results in the GVF Snake model cells being adsorbed to the wrong position on the edges. In this paper, the following methods are studied: firstly, the adaptive threshold is used to remove the background of the cell, then the information of the direction of the ray gradient is used to calculate the gray value of the cell. Finally, according to the gray value on the ray, the GVF Snake model is used to evolve. In the evolution process, using the stack gray difference compensation algorithm, combining with the forward gray difference suppression, can overcome the noise very well. The effect of false edge information such as blood cells and inflammatory cells. The Herlev database used in this paper verifies the effectiveness and feasibility of this method. On the basis of accurate segmentation of cervical cell images, The morphological parameters of cervical cell images were studied, including 9 geometric features and 4 texture features, including the circumference of the cytoplasm, the circumference of the nucleus, the longest axis in the vertical direction, and the widest axis in the horizontal direction, respectively. The ratio of nucleus to cytoplasm, the longest length from the axis center to the circumference, the average length from the axis center to the perimeter, the longest length from the center of gravity to the perimeter, and the average length from the center of gravity to the circumference are four texture features: the entropy of the symbiotic matrix, The contrast, contrast and roughness of the symbiotic matrix. The single cell image of the cervix can be transformed from the nucleus, the cytoplasm, and the background into polar coordinates. The gray values of 360 poles in polar coordinates are extracted to form a feature matrix. In this paper, the feature vectors in polar coordinates are fused with the former morphological features. Using vector machine based on the combination of AdaBoost and SVM algorithm to improve the stability and difference of the classifier. The AdaBoost-SVM classifier is used to fuse the extracted multiple features of cervical cells. The combination of double classifiers can make up for the shortcomings of single classifier and improve the efficiency of classification recognition. The method of feature extraction is combined with AdaBoost-SVM multi-feature fusion classifier. The results show that the efficiency and accuracy of cervical smear screening are improved, and the misdiagnosis rate of cervical cancer is reduced. Systematic research and improvement of feature extraction and classification and recognition of cervical cells have been carried out. The experimental results show that the method presented in this paper can accomplish the quantitative analysis of cervical cells. It has good application value for automatic screening and analyzing system of cervical cell image.
【學位授予單位】：廣西師范大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：R737.33;TP391.41

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