本文選題：紅細(xì)胞分類 + 數(shù)學(xué)建模　； 參考：《湘潭大學(xué)》2017年碩士論文

【摘要】：隨著經(jīng)濟(jì)的發(fā)展,人們的生活質(zhì)量和文化水平不斷提升,大家越來越重視自身及家人的健康問題,人體健康情況與紅細(xì)胞的形態(tài)和數(shù)量息息相關(guān),許多疾病的診斷必須依靠紅細(xì)胞形態(tài)與數(shù)量作為依據(jù)。本文根據(jù)紅細(xì)胞自動(dòng)識(shí)別系統(tǒng)的作業(yè)要求,運(yùn)用圖像處理及模式識(shí)別技術(shù),對(duì)其中的關(guān)鍵技術(shù)進(jìn)行研究,用實(shí)驗(yàn)進(jìn)行論證,較好地解決了紅細(xì)胞分類識(shí)別的問題。本文針對(duì)紅細(xì)胞顯微圖像自動(dòng)識(shí)別系統(tǒng)的研究課題,在前人工作的基礎(chǔ)上,對(duì)紅細(xì)胞語義模型的建立、紅細(xì)胞圖像分割算法、紅細(xì)胞特征提取與選擇、模糊聚類分類識(shí)別等方面進(jìn)行研究和實(shí)驗(yàn),主要完成以下工作:1、在圖像分割研究方面,在分析紅細(xì)胞圖像特點(diǎn)及部分分割算法后,提出了一種基于圖割的紅細(xì)胞分割算法,此算法是通過迭代的方式將目標(biāo)從一個(gè)復(fù)雜的背景中提取出來,解決了無關(guān)區(qū)域和內(nèi)部空洞對(duì)分割的影響和多粘連細(xì)胞的分割效果差的問題,減少了算法的復(fù)雜性,提高了分割速度和分割準(zhǔn)確率。2、在形狀特征提取中,研究了各類異形紅細(xì)胞的基本形態(tài),然后逐類建立語義模型,根據(jù)語義模型確定模型特征的數(shù)學(xué)表述方法,提出了邊緣突起點(diǎn)和凹點(diǎn)兩個(gè)新的形狀特征,然后對(duì)其他形狀特征進(jìn)行改善和選擇,為進(jìn)一步識(shí)別各種異形紅細(xì)胞提供了依據(jù),并且提高了運(yùn)行效率和分類準(zhǔn)確率。3、在紋理特征提取中,采用共生矩陣分析法描述紅細(xì)胞的紋理特征,經(jīng)過實(shí)驗(yàn)數(shù)據(jù)的對(duì)比,選取了基于共生矩陣的二階矩、對(duì)比度、相關(guān)性、熵、逆差矩、和熵、均值差、差熵、方差和9個(gè)紋理特征。4、學(xué)習(xí)了模糊聚類的基本原理,研究了FCM聚類算法,針對(duì)其算法在模糊距離計(jì)算方面的不足進(jìn)行了改進(jìn),提高了算法的速度和準(zhǔn)確度。采用改進(jìn)的FCM算法對(duì)11類紅細(xì)胞進(jìn)行聚類識(shí)別,對(duì)聚類結(jié)果進(jìn)行了分析。實(shí)驗(yàn)證明采用改進(jìn)的聚類算法實(shí)現(xiàn)了代碼的高效運(yùn)行,提高了圖像數(shù)據(jù)處理的效率,滿足圖像實(shí)時(shí)處理的工程需求,完成了對(duì)紅細(xì)胞的分類,得到較好的分類效果。
[Abstract]:With the development of economy, people's quality of life and educational level are improving constantly. People pay more and more attention to the health problems of themselves and their families. Human health is closely related to the shape and quantity of red blood cells. The diagnosis of many diseases must depend on the morphology and quantity of red blood cells. According to the operational requirements of the red blood cell automatic recognition system, this paper studies the key technology by using image processing and pattern recognition technology, and proves it by experiment, which solves the problem of red blood cell classification and recognition. In this paper, aiming at the research topic of erythrocyte microscopic image automatic recognition system, on the basis of previous work, the establishment of erythrocyte semantic model, red cell image segmentation algorithm, red blood cell feature extraction and selection, In the aspect of image segmentation, after analyzing the characteristics of red blood cell image and partial segmentation algorithm, a red blood cell segmentation algorithm based on graph cutting is proposed. The algorithm extracts the target from a complex background by iterative method, solves the problem of the influence of irrelevant region and internal cavity on segmentation and the poor segmentation effect of multi-adhesion cells, and reduces the complexity of the algorithm. The segmentation speed and segmentation accuracy are improved. In shape feature extraction, the basic morphology of all kinds of special-shaped red blood cells is studied, and then the semantic model is established one by one, and the mathematical expression method of the model feature is determined according to the semantic model. Two new shape features, starting point of edge process and concave point, are proposed, and other shape features are improved and selected, which provides a basis for further recognition of various special-shaped red blood cells. In texture feature extraction, the co-occurrence matrix analysis method is used to describe the texture features of red blood cells. Through the comparison of experimental data, the second moment, contrast and correlation based on co-occurrence matrix are selected. Entropy, deficit moment, and entropy, mean difference, difference entropy, variance and nine texture features. 4. The basic principle of fuzzy clustering is studied, and the FCM clustering algorithm is studied. The speed and accuracy of the algorithm are improved. The improved FCM algorithm is used to identify 11 red blood cells and the clustering results are analyzed. The experimental results show that the improved clustering algorithm can efficiently run the code, improve the efficiency of image data processing, meet the engineering requirements of real-time image processing, and complete the classification of red blood cells.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R446.1;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 張嘉桐;李雪妍;郭樹旭;康建玲;;基于橢圓傅里葉描述子的形狀表示的研究[J];計(jì)算機(jī)工程與應(yīng)用;2014年02期

2 張瑞華;吳謹(jǐn);;基于邊緣鏈碼信息的黏連細(xì)胞分割算法[J];北京理工大學(xué)學(xué)報(bào);2013年07期

3 劉松濤;殷福亮;;基于圖割的圖像分割方法及其新進(jìn)展[J];自動(dòng)化學(xué)報(bào);2012年06期

4 譚勇;朱斌;;基于改進(jìn)GAC模型的血細(xì)胞顯微圖像分割[J];計(jì)算機(jī)仿真;2011年07期

相關(guān)碩士學(xué)位論文 前1條

1 王職軍;細(xì)胞特征提取及聚類算法研究與DSP實(shí)現(xiàn)[D];湘潭大學(xué);2014年

，

本文編號(hào)：1922468