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研究生:黃宇睿
研究生(外文):Yu-Ruei Huang
論文名稱:以細胞學影像分析系統與支援向量機為基礎的子宮頸抹片細胞影像之研究
論文名稱(外文):The Study of Uterine Cervical Cellular Images Based on PC-based Cytopathologic Image Analysis System and Support Vector Machine
指導教授:陳 榮 靜
指導教授(外文):Rung-Ching Chen
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:50
中文關鍵詞:影像分析細胞學子宮頸癌細胞分析支援向量機
外文關鍵詞:cytologyImage analysiscervical cancerSupport vector machine
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細胞學的篩選已經廣泛地用於控制子宮頸癌的流行,然而,利用子宮頸抹片細胞篩選仍有一些盲點來自於採檢、篩檢以及判讀的誤差,可能會使得敏感度或精準度下降。在本研究中,我們主要設計一個能在例行細胞學實驗室中現有之軟硬體設備即可操作執行的細胞影像分析系統,供臨床判斷與研究工具使用。
最初我們利用顯微鏡及數位相機拍攝超柏氏抹片,共拍攝1814個細胞影像,每個細胞分別利用100、200與400倍的倍率拍攝,且依照四種診斷種類分類,分別為正常細胞,包括Superficial squamous cell (S Cell)、Intermediate squamous cell (I Cell)、parabasal squamous cell (P Cell)與異常細胞,包括Low grade squamous intraepithelial lesion (LSIL Cell)與high grade squamous intraepithelial lesion (HSIL)兩種類型癌症細胞。之後,經由病理學家討論一致通過之後,評選其中的503個放大倍率為400倍的細胞影像,供未來實驗。
我們設計的細胞影像分析系統,能自動批次輸入細胞影像,將彩色細胞影像轉成灰階影像,並針對13個主要的細胞型態參數,如細胞核大小、形狀、核質比與核染值做客觀的運算分析。我們利用支援向量機(support vector machine, SVM)對細胞影像分析系統所產生的數據做訓練(Training)與預測(predict)。
結果顯示我們評估後所選擇的細胞型態參數能正確的將異常細胞從正常細胞群中分辨出來(statistic significance p<0.001)。 SVM的預測結果能得到高準確率的分類(accuracy up to 94.43%, sensitivity: 100% and specificity: 98.5%, 沒有 false negative 的例子)。
我們所提議的系統能有效並準確的判斷細胞病理,是能提供婦科醫學實驗用來做來評估的廉價且準確的定量工具。且此細胞分析軟體也可作為人體其他細胞或其他生物細胞型態分析研究,對我國生物科技與醫學工程的研究發展,提供一項強而有力的資訊化影像檔案分析工具。
Cytologic screening has been widely used for controlling the prevalence of cervical cancer. However, errors from sampling, screening and interpretation, still concealed some unpleasant results. This study aims to design a cellular image analysis system with feasible and available software and hardware for a routine cytologic laboratory and for both clinical and research utility. Originally, 1814 cellular images, divided into 4 diagnostic categories: superficial cell, intermediate cell, parabasal cell, dysplastic cell including low grade and high grade squamous intraepithelial lesion (LSIL and HSIL), from the liquid-based cervical smears with Papanicolaou stain, in 100,200 and 400 microscopic magnification were captured by digital camera. After review by pathologic experts, 503 images with peer agreement of cellular classification, were selected for further study. Automatically, self-designed computer program will analyze gray-level images by 13 morphometric parameters focusing on the objective measurement of nuclear size, nuclear shape, N/C ratio and chromatin pattern. Classification of signature patterns using a novel statistical support vector machine (SVM) based algorithm was created. Results show the selected morphometric parameters can be correctly differentiated the dysplastic cells from normal cells (statistic significance p<0.001). SVM classifier can achieve a high accuracy for cellular classification (accuracy up to 94.43%, sensitivity: 100% and specificity: 98.5%, no false negative cases). Conclusively, the proposed system can provide an inexpensive, feasible, accurate, quantitative tool for evaluating gynecologic specimens.
Index of Contents
中文摘要 I
Abstract III
誌謝 V
Index of Contents VII
List of Figures IX
List of Tables X
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Objective 4
1.3 The Framework of Thesis 5
Chapter 2 Materials and Methods 6
2.1 Experimental Related Work 6
2.2 Acquisition of cell images 8
2.3 Cellular image filename processing 9
2.4 Image editing and processing 10
2.5 Objective micrometer image 12
2.6 PC-based cytopathologic image analysis (PCCIA) system 13
2.7 Support Vector Machine 13
Chapter 3 PC-based cytopathologic image analysis (PCCIA) system 17
3.1 Contour detection using circular seeded region growing method 18
3.2 Capture the texture with mask image 21
3.3 Measurement and analysis of cellular morphology and texture 22
3.3.1 The morphometric parameters 22
3.3.2 The texture parameters 24
3.3.2.1 Co-occurrence matrix 24
3.3.2.2 Tamura features 24
Chapter 4 Experiment and Discussion 27
4.1 Experiment sample 27
4.2 Evaluate the morphometric parameters 28
4.3 Classification of cell types using SVM 31
4.4 Results 35
Chapter 5 Conclusion and Future Work 40
5.1 Discussion and Conclusion 40
5.2 Future Work 43
Reference 43
Appendix 50
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