【摘要】：隨著計(jì)算機(jī)技術(shù)的發(fā)展及交叉學(xué)科在各領(lǐng)域的廣泛應(yīng)用,基于機(jī)器視覺的顯微圖像自動(dòng)識(shí)別技術(shù)也在醫(yī)學(xué)領(lǐng)域中得到迅猛發(fā)展,不僅為臨床診斷提供了一個(gè)可靠高效的工具,同時(shí)也為醫(yī)學(xué)科研和教學(xué)帶來了方便。寄生蟲卵檢測是醫(yī)學(xué)中重要檢測項(xiàng)目之一,傳統(tǒng)寄生蟲卵檢測大多采用人工鏡檢方式,不僅效率低,工作強(qiáng)度大,而且受操作人員主觀影響較大,無法保存完整的病例資料,不利于臨床動(dòng)態(tài)觀察。基于機(jī)器視覺的自動(dòng)檢測不僅效率高,無污染,而且適應(yīng)現(xiàn)代醫(yī)療信息化發(fā)展的需求,因此本文在前人對(duì)寄生蟲卵、細(xì)胞顯微圖像識(shí)別研究的基礎(chǔ)上,著重研究了基于機(jī)器視覺的寄生蟲卵顯微圖像采集到識(shí)別的過程。具體研究內(nèi)容如下:1.著重介紹了基于機(jī)器視覺的寄生蟲卵顯微圖像自動(dòng)識(shí)別系統(tǒng)的硬件結(jié)構(gòu)設(shè)計(jì),包括蟲卵標(biāo)本的自動(dòng)采集裝置、顯微鏡的自動(dòng)拍攝裝置、照明系統(tǒng)、CCD相機(jī)、圖像采集卡等。2.針對(duì)寄生蟲卵顯微圖像高噪聲的特點(diǎn),研究圖像預(yù)處理及分割方法,并提出了一種改進(jìn)的二維最大熵閾值遺傳算法結(jié)合形態(tài)學(xué)分割圖像的優(yōu)化算法,能準(zhǔn)確快速的實(shí)現(xiàn)寄生蟲卵顯微圖像的除噪分割。3.詳細(xì)介紹了寄生蟲卵顯微圖像的幾種常見特征類型,并研究提取各類特征的方法,找到一組最佳的特征組合作為寄生蟲卵顯微圖像的輸入量來表征各類典型寄生蟲卵顯微圖像的特點(diǎn)。4.研究幾種智能識(shí)別算法,如BP(Back Propagation)神經(jīng)網(wǎng)絡(luò),SVM(Support Vector Machine),針對(duì)提取的寄生蟲卵顯微圖像特征,分別設(shè)計(jì)了基于BP神經(jīng)網(wǎng)絡(luò)的分類器和基于SVM分類器,并且統(tǒng)計(jì)了各自識(shí)別的精確度,然后將這兩種分類器的識(shí)別率作對(duì)比,從中選出高精度高效率的分類器。通過本文的研究和試驗(yàn),證明基于機(jī)器視覺的寄生蟲卵顯微圖像自動(dòng)識(shí)別技術(shù)是可行的、有效的,在醫(yī)學(xué)上具有一定的實(shí)用價(jià)值。
[Abstract]:With the development of computer technology and the wide application of interdiscipline in various fields, the automatic recognition technology of microscopic image based on machine vision has been developed rapidly in the field of medicine, which not only provides a reliable and efficient tool for clinical diagnosis. At the same time, it also brings convenience to medical research and teaching. The detection of parasite eggs is one of the most important items in medicine. The traditional examination of parasite eggs is mostly by artificial microscope, which is not only low efficiency and high working intensity, but also can not preserve the complete case data due to the subjective influence of the operators. It is unfavorable to clinical dynamic observation. The automatic detection based on machine vision is not only efficient and pollution-free, but also adapt to the needs of modern medical information development. Therefore, this paper is based on the research of microscopic image recognition of parasite eggs and cells. The process of parasite egg microscopic image acquisition and recognition based on machine vision is studied. The specific research contents are as follows: 1. This paper mainly introduces the hardware structure design of the parasite egg microscopic image recognition system based on machine vision, including the automatic collection device of egg specimen, the automatic shooting device of microscope, the illumination system, the CCD camera, the image acquisition card and so on. In view of the high noise characteristics of parasite egg microscopic image, the image preprocessing and segmentation methods are studied, and an improved two-dimensional maximum entropy threshold genetic algorithm combined with morphological image segmentation optimization algorithm is proposed. Can accurately and quickly realize the parasite egg microscopic image denoising segmentation. 3. In this paper, several common characteristic types of parasite egg microscopic image are introduced in detail, and the methods of extracting all kinds of characteristics are studied. To find out a group of best feature combinations as the input amount of parasite egg microscopic image to characterize the characteristics of various typical parasite egg microscopic images. 4. This paper studies several intelligent recognition algorithms, such as BP (Back Propagation) neural network, SVM (Support Vector Machine), which designs classifiers based on BP neural network and SVM classifier, respectively, according to the features of extracted parasite egg microscopic images. The recognition accuracy of the two classifiers is analyzed, and the recognition rates of the two classifiers are compared to select the high precision and high efficiency classifiers. Through the research and experiment in this paper, it is proved that the automatic recognition technology of parasite egg microscopic image based on machine vision is feasible and effective, and has certain practical value in medicine.
【學(xué)位授予單位】：河南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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