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研究生:王怡珺
研究生(外文):Yi-Jung Wang
論文名稱:聽覺神經細胞核之辨識與分佈分析
論文名稱(外文):The Identification and Distribution Analysis for Nuclei of Hearing Neurons
指導教授:鄭國順鄭國順引用關係
指導教授(外文):Kuo-sheng Cheng
學位類別:碩士
校院名稱:國立成功大學
系所名稱:醫學工程研究所碩博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:58
中文關鍵詞:碎形分析細胞辨識
外文關鍵詞:fractal analysisnuclei identification
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本研究中的影像來自於幼鼠的腦下丘組織切片,經由不同的頻率刺激之後,
聽覺神經細胞在經過染色技術後,可觀察到其神經細胞的分佈情形會有所不同,在生理學的研究上則代表不同的生理特性。
傳統上,生理學家在作組織切片的影像分析時,絕大多數都是採用人工辨識
的方式,也就是用肉眼觀察法。此方法不但費時而且結果也因人而異,因此準確性較存疑。本研究的主要目的在於希望發展出一套自動辨識影像系統,將人為判斷時的特徵轉換為量化的參數,加入到程式中作辨識。期望借著人性化的程式設計,能將被染色的聽覺神經細胞較正確且自動地辨識出來。
另一方面,已辨識出來的神經細胞再介由統計及碎形維度的分析,希望找到一組量化的參數,來量化並探討其神經細胞二維分佈的情形,希望能提供生理學者一個參考的依據。
此外,我們也希望將這些被染色的神經細胞作一些形態學上的分析,探討是否這些神經細胞具有不同的形態特徵,分屬於不同的細胞族群。
The images in our study come from the biopsy of inferior collicular (IC) in young rat. By different frequencies exciting, these hearing neurons will display different distribution after staining. In physiology, it may present different physiology characters.
Traditionally, the physiologists analyze the image of tissue slide using manual identification approach. This way is not only slow but also experience-dependent. Hence it is not very accurate. In this study, an automatic identification system that integrates the a priori knowledge of manual identification is developed. It is to identify these neurons more accurate and automatic. Then, the statistic and fractal dimension analysis are applied to investigate the 2-D distribution of these neurons. In addition, the morphology of these neurons is also analyzed to differentiate their characteristics.
Chinese Abstract i
Abstract ii
Acknowledgments iii
Contents iv
List of Tables vi
List of Figures vii
List of Symbol ix
Chapter 1. Introduction 1
1.1 Background 1
1.2 Motivation 4
1.3 Literatures review 4
1.3.1 Image segmentation 4
1.3.2 Morphological analysis 6
1.4 Purposes 7
Chapter 2. Materials and Methods 10
2.1 System environment 10
2.2 Image acquisition 11
2.3 Image preprocessing 13
2.3.1 Image transformation 13
2.3.2 Image filtering 15
2.4 Image segmentation 17
2.5 Nuclei identification 18
2.5.1 Knowledge integration 18
2.5.2 Logistic function 21
2.6 Distribution analysis 25
2.6.1 Statistical analysis 25
2.6.2 Fractal analysis 26
2.7 Morphological analysis 29
Chapter 3. Results 30
3.1 Image transformation 30
3.2 Image filtering 31
3.3 Image segmentation 31
3.4 Nuclei identification 33
3.5 Distribution 38
3.5.1 Statistical analysis 38
3.5.2 Fractal analysis 43
3.6 Morphological analysis 48
Chapter 4. Discussion 50
4.1 Identification accuracy 50
4.2 The relationship between fractal dimension and neurons distribution 51
4.3 Morphology of hearing neuron nucleus 53
Chapter 5. Conclusion and Prospects 55
References 56
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