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研究生:郭修瑞
研究生(外文):Hsiu-Jui Kuo
論文名稱:使用特徵選擇演算法暨支援向量機在胃部組織學分類上的研究與應用
論文名稱(外文):Using Feature Selection with Support Vector Machine in Gastric Histology Classification
指導教授:詹寶珠詹寶珠引用關係
指導教授(外文):Pau-Choo Chung
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:65
中文關鍵詞:小波轉換內視鏡影像
外文關鍵詞:feature selectionfeature extractionendoscopicfeature classification
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  這份論文提出一個電腦輔助診斷的系統,透過特徵選擇演算法 sequential forward floating selection (SFFS)暨支援向量機,來輔助醫師,直接從胃部內視鏡影像中讀出組織學上的結果,避免因組織切片所造成的傷害與傳統上組織培養所耗費的時間。
  在取得的內視鏡影像中,我們首先利用數位小波轉換、顏色及材質等方法來取得所需的影像特徵,由於取得的影像特徵數目眾多且無法有效表達內視鏡影像的內容,我們利用特徵選擇演算法SFFS暨支援向量機來找出在分類上最有意義的影像特徵。接下來再以這些選取出來的特徵作為支援向量機輸入而得到組織學上的結果。
  透過這些方法,我們實做出一個新的診斷系統,可以輔助醫師立即從內視鏡影像中判別出組織學上的結果,而不需要耗時的切片培養。
  This study presented a computer-aided diagnosis system using sequential forward floating selection (SFFS) with support vector machine (SVM) to assist physicians to obtain gastric histology from endoscopic images without invasive biopsy during endoscopy. At first, several images features of endoscopic images are extracted via discrete wavelet transform, color and texture criterion. Then, SFFS is applied to select a subset of features, which performs the best classification result under SVM. Based on this methodology, a new diagnosis system is implemented to provide physicians the instant gastric histology results during the endoscopy without invasive biopsy.
LIST OF TABLES VI
LIST OF FIGURES VII
Chapter1 Introduction 1
Chapter2 Feature Extraction 6
2.1 Pre-processing with Different Color Spaces 6
2.2 Color and Texture Criteria 9
2.3 Wavelet transform and feature extraction 12
2.3.1 Discrete wavelet transform and multi-resolution analysis 14
2.3.2 Statistics on the wavelet domain as image features 18
Chapter3 Feature Selection 20
Chapter4 Feature Classification 25
4.1 Some theoretical backgrounds of learning to classify 27
4.2 VC theory and structural risk minimization principle 28
4.3 VC dimension in practice 30
4.4 Relation between margins and VC dimension 30
4.5 Nonlinear mapping to kernel feature space 32
4.6 Support vector classification 33
4.7 Feature classification using SVM with different kernels 37
Chapter5 Experimental Results 39
5.1 Patients and study design 39
5.2 Experiments of feature selection and classification 41
5.3 Comparison of SVM and multi-layer perceptron neural network 42
5.4 Comparison of RBF kernel and polynomial kernel 45
5.5 Comparison of SFFS and SFS 48
5.6 Finding proper thresholds in SVM 53
Chapter6 Conclusions 61
Reference 62
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