本文選題：宮頸細(xì)胞圖像　切入點(diǎn)：圖像識別　出處：《哈爾濱理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：宮頸癌是導(dǎo)致女性死亡的第二大惡性腫瘤,大約占總的癌癥死亡率的十分之一。據(jù)WHO統(tǒng)計(jì),2015年全球有51.8萬新增病例和27.3萬死亡病例。2015年,我國有9.89萬新增病例和3.05萬死亡病例;而美國僅有1.29萬新增病例和0.41萬死亡病例。預(yù)計(jì)到2050年,我國將會有18.7萬新增病例。因此,尋找適合于我國的宮頸癌的篩查方法已經(jīng)迫在眉睫。宮頸細(xì)胞圖像識別技術(shù)是近年來興起的一種新的宮頸細(xì)胞識別的方法。該方法克服了傳統(tǒng)的人工判讀篩查方式存在成本高、工作量大、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。該方法首先采用液基薄層制片技術(shù)將脫落細(xì)胞制片,然后在顯微鏡視野下通過工業(yè)相機(jī)抓取細(xì)胞圖像進(jìn)行分析識別。不僅能夠用于檢測異常上皮細(xì)胞,而且能夠應(yīng)用于宮頸癌的篩查診斷。本文采用宮頸細(xì)胞圖像識別技術(shù)輔助病理學(xué)醫(yī)生診斷,最終目的是識別出宮頸內(nèi)異常上皮細(xì)胞,減輕醫(yī)生的工作量、降低診斷結(jié)果中的假陰性和假陽性。宮頸細(xì)胞圖像識別方法主要包括宮頸細(xì)胞圖像獲取、宮頸細(xì)胞圖像預(yù)處理及分割、宮頸細(xì)胞圖像特征提取和宮頸細(xì)胞圖像分類四個(gè)階段。本文對宮頸細(xì)胞圖像識別方法以下幾個(gè)方面進(jìn)行改進(jìn):在宮頸細(xì)胞圖像類別劃分方面:本文結(jié)合宮頸取材特點(diǎn),提出了兩種劃分方法。第一種類別劃分方法,首先將宮頸細(xì)胞圖像分為上皮細(xì)胞、淋巴細(xì)胞、中性粒細(xì)胞和垃圾細(xì)胞,然后將上皮細(xì)胞分為正常上皮細(xì)胞和異常上皮細(xì)胞,并將該宮頸細(xì)胞圖像劃分方法應(yīng)用于SMMCFBCCI分類器。第二種類別劃分方法,將宮頸細(xì)胞圖像直接劃分為正常上皮細(xì)胞、異常上皮細(xì)胞、中性粒細(xì)胞、淋巴細(xì)胞和垃圾細(xì)胞,并將該宮頸細(xì)胞圖像類別劃分方法應(yīng)用于PMMCFBCCI分類器。在宮頸細(xì)胞圖像特征選取方面:本文結(jié)合了前人研究和宮頸細(xì)胞學(xué)特點(diǎn),提出了NF和PF特征集合。其中,NF是根據(jù)宮頸細(xì)胞病理特征定義的常規(guī)特征集合,共22維;PF是指傳統(tǒng)方法中沒有考慮到的潛在特征集合,共14維。然后,采用Relief F算法選擇出類別相關(guān)性高的24維特征。在宮頸細(xì)胞圖像分類器設(shè)計(jì)方面:結(jié)合多分類器融合方法,本文提出了SMMCFBCCI分類器和PMMCFBCCI分類器。SMMCFBCCI分類器是基于兩級級聯(lián)的多分類器融合方法。其中,第一級粗分類采用C4.5分類器;第二級細(xì)分類采用LR分類器。PMMCFBCCI分類器是基于多數(shù)投票法的串行分類器融合。首先,采用NB、C4.5及KNN分類器得到預(yù)測結(jié)果;然后,采用多數(shù)投票法得到最終預(yù)測結(jié)果。
[Abstract]:Cervical cancer is the second largest malignant tumor in women, accounting for 1/10% of the total cancer mortality. According to WHO statistics, in 2015, there were 518,000 new cases and 273,000 deaths in the world. On 2015, there were 98,900 new cases and 30,500 deaths in China. In the United States, there are only 12,900 new cases and 4,100 deaths. By 2050, there are expected to be 187,000 new cases in China. It is urgent to find a suitable screening method for cervical cancer in China. The technology of cervical cell image recognition is a new method of cervical cell recognition developed in recent years. This method overcomes the high cost of traditional manual screening. The workload, reliability and accuracy are influenced by the professional technique and subjective emotion of the physician. Firstly, the technique of thin-film preparation based on liquid is used to prepare the shedding cells. Then under the microscope field of vision, the cells are captured by industrial cameras for analysis and recognition. Not only can they be used to detect abnormal epithelial cells, And it can be used in the screening and diagnosis of cervical cancer. In this paper, cervical cell image recognition technology is used to assist pathologist in diagnosis, and the ultimate goal is to identify abnormal epithelial cells in the cervix and reduce the workload of doctors. The methods of cervical cell image recognition mainly include cervical cell image acquisition, cervical cell image preprocessing and segmentation. There are four stages of cervical cell image feature extraction and cervical cell image classification. In this paper, the methods of cervical cell image recognition are improved in the following aspects: classification of cervical cell image: this paper combines the characteristics of cervical sampling, Two methods of classification are proposed. The first is to divide cervical cell images into epithelial cells, lymphocytes, neutrophils and garbage cells, then to classify epithelial cells into normal epithelial cells and abnormal epithelial cells. The cervical cell image was divided into normal epithelial cells, abnormal epithelial cells, neutrophils, lymphocytes and garbage cells. The method of classification of cervical cell image is applied to PMMCFBCCI classifier. In the aspect of feature selection of cervical cell image, this paper combines the previous studies and the characteristics of cervical cytology. In this paper, NF and PF feature sets are proposed, in which NF is a conventional feature set defined according to the pathological features of cervical cells, and 22 dimensional PF refers to the potential feature set which is not considered in the traditional method, which has a total of 14 dimensions. The Relief F algorithm is used to select 24 dimensional features with high class correlation. In the design of cervical cell image classifier, the fusion method of multiple classifiers is combined. In this paper, SMMCFBCCI classifier and PMMCFBCCI classifier SMMCFBCCI are proposed, which are multi-classifier fusion method based on two-stage cascade, in which C4.5 classifier is used in the first stage coarse classification. In the second stage, LR classifier. PMMCFBCCI classifier is a serial classifier fusion based on majority voting method. First, NBC4.5 and KNN classifier are used to obtain the prediction results, and then the final prediction results are obtained by majority voting method.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R737.33;TP391.41

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