據(jù)觀察線粒體疾病一般是由線粒體DNA或天然DNA的遺傳或突變引起的,這些疾病會(huì)使線粒體中的蛋白質(zhì)或RNA分子的原始功能受到影響。線粒體細(xì)胞疾病有可能干擾生物體的正常功能,甚至導(dǎo)致生物體死亡,因此,有必要對線粒體細(xì)胞疾病進(jìn)行檢測,找出預(yù)防措施。通過線粒體細(xì)胞顯微圖像可以檢測出其細(xì)胞形態(tài),綜合分析這些圖像可以用來檢測患病細(xì)胞,進(jìn)而對線粒體疾病的未來行為進(jìn)行預(yù)測與分類。面對這一問題人眼難以準(zhǔn)確分辨這些細(xì)胞圖像中的細(xì)微差別,而人工智能可以在檢測這些圖像中隱藏的固有模式方面發(fā)揮重要作用,通過對顯微鏡圖像數(shù)據(jù)智能化分析,可以提前識(shí)別疾病,從而為臨床和疾病問題提供解決方案。鑒于正常和受影響的線粒體細(xì)胞具有不同的形態(tài)特征,疾病改變了線粒體細(xì)胞的形態(tài),檢測細(xì)胞形態(tài)的變化以及與此變化相關(guān)的時(shí)間具有重要的生物學(xué)意義。我們主要研究分析線粒體細(xì)胞的顯微圖像,主要研究成果和創(chuàng)新點(diǎn)如下:1提出了一種正常的和藥物處理的細(xì)胞圖像關(guān)聯(lián)分析算法,簡稱為IC。實(shí)驗(yàn)結(jié)果表明,該方法具有較好的相關(guān)性。2.提出了一種基于卷積神經(jīng)網(wǎng)絡(luò)的正常和藥物處理的細(xì)胞圖像識(shí)別算法·通過實(shí)驗(yàn)驗(yàn)證了分類的準(zhǔn)確性,并與傳統(tǒng)的方法進(jìn)行了比較。3.分析了...

【文章頁數(shù)】：128 頁

【學(xué)位級(jí)別】：博士

【文章目錄】：
摘要
Abstract
ACKNOWLEDGEMENTS
1 INTRODUCTION
    1.1 RESEARCH PROBLEM
    1.2 RESEARCH MOTIVATION
    1.3 AIMS AND OBJECTIVES
        1.3.1 Aim
        1.3.2 Hypothesis
        1.3.3 Objective
    1.4 METHODOLGY
    1.5 THESIS CONTRIBUTIONS
    1.6 THESIS ORGANIZATION
2 MITOCHONDRIAL CELL
    2.1 INTRODUTCION
    2.2 MITOCHONDRIAL CELL
    2.3 MITOCHONDRIAL CELL FUNCTION
    2.4 MITOCHONDRIAL CELL PARTS
        2.4.1 Mitochondrial DNA
        2.4.2 Mitochondrial Membranes
        2.4.3 Mitochondrial Spaces
        2.4.4 Mitochondrial Reproduction
        2.4.5 Ribosomes
        2.4.6 Cell Nucleus
    2.5 TWO PHOTON EXCITED FLUORESCENCE IMAGES
    2.6 MITOCHONDRIAL CELLS IMAGE CLASSIFICATION TECHIQUES
    2.7 DRUG AND NORMAL CELL
    2.8 DISEASES AND NORMAL CELL
    2.9 MITOCHONDRIAL MOVEMENT
    2.10 CONCLUSION
3 NORMAL,DRUG TREATED CELL IMAGES RECOGNATION ANDCORRELATION
    3.1 INTRODUCTION
    3.2 CELL RECOGNITION
        3.2.1 Data Set
        3.2.2 Cell Recognition Method
        3.2.3 Results of Cell Recognition Method
    3.3 IMPROVE DIGITAL IMAGE CORRELATION
        3.3.1 Data Set
        3.3.2 Method
        3.3.3 Result of Improve Digital Image Correlation (IC)
    3.4 CONCLUSION
4 DRUG AND NORMAL CELLS IMAGES CLASSIFICATION (DNCIC)
    4.1 INTRODUCTION
    4.2 METHOD
        4.2.1 Data Set
        4.2.2 Method Explanation
    4.3 RESULTS
        4.3.1 Newly Developed CNN Method for Mitochondrial Cell Images with High Accuracy
        4.3.2 Classification of Drug Treated and Normal cells
    4.4 DISCUSSION
    4.5 CONCLUSION
5 NORMAL AND DISEASED CELLS CLASSIFICATION (NDCC)
    5.1 INTRODUCTION
    5.2 METHOD
        5.2.1 Data Set
        5.2.2 Normal and Diseases Cells Classification
        5.2.3 CNN Algorithm
    5.3 RESULTS
        5.3.1 Heterogeneity Between Cells
        5.3.2 Variation Between Normal and Diseases Cells
        5.3.3 Histogram of Features and Classification of Diseased and Normal Cells
        5.3.4 Distinguishes Diseases Patches
        5.3.5 Model Development Diseases Patches
        5.3.6 Classification of Diseases and Normal Cell Region
    5.4 DISCUSSION
    5.5 CONCLUSION
6 MITOCHONDRIAL ORGANELLE MOVEMENT CLASSIFICATION(MOMC)
    6.1 INTRODUCTION
    6.2 METHOD
        6.2.1 Mitochondria Organelle Movement Classification (MOMC)
        6.2.2 Description of CNN Inception-V3 for Classification of Different Shape of Mitochondria (fission and fusion)
        6.2.3 Proposed Method
        6.2.4 Statistics and Reproducibility
    6.3 RESULTS
        6.3.1 Data Set
        6.3.2 Mitochondrial Dynamics
        6.3.3 Classification of Mitochondria Shape Whole Slide Images
    6.4 DISCUSSION
    6.5 CONCLUSION
7 CONCLUSIONS
    7.1 KEY CONCLUSIONS OF THE RESEARCH
    7.2 FUTURE SCOPE
REFERENCES
PUBLICATIONS



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