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研究生:林彥賓
研究生(外文):Lin, YenPin
論文名稱:偵測大腸鏡腫瘤系統
論文名稱(外文):Tumor Detecting in Colonoscopic Narrow Band Imaging Data
指導教授:陳偉銘陳偉銘引用關係
指導教授(外文):Chen, WeiMing
口試委員:陳偉銘黃德成沈偉誌
口試委員(外文):Chen, WeiMingHuang, DerChenShen, WeiChih
口試日期:2012-07-26
學位類別:碩士
校院名稱:國立宜蘭大學
系所名稱:資訊工程研究所碩士班
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:2012
畢業學年度:100
語文別:中文
論文頁數:32
中文關鍵詞:窄頻影像大腸鏡腫瘤稀疏表示約束高斯模糊向量量化編碼
外文關鍵詞:Narrow Band Imaging SystemPolyps DetectionSparse RepresentationBounded GaussianVector Quantization
相關次數:
  • 被引用被引用:0
  • 點閱點閱:476
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  • 下載下載:33
  • 收藏至我的研究室書目清單書目收藏:1
近年來大腸癌成為了全球癌症的第三大癌症。然而這種疾病可以透過大腸鏡的檢查,可以有效的檢測出息肉、腫瘤的所在位置。從一般的大腸鏡影像上看,息肉主要分為二種,一種為增生性息肉是屬於良性病變,而另一種腺瘤性息肉則是有可能成為癌症病變。最常見的做法是切除所有已發現的息肉,並將切割下來的息肉送往病理分析。但去除增生性息肉容易給患者帶來不必要的風險以及在病理分析上花費不必要的成本。因此在本論文中,透過窄頻影像的特性,嘗試開發了一種新型的大腸鏡影像腫瘤的切割系統。此系統的主要是運用於息肉切割的分類方法,此方法必需要找出需透過手術切割的息肉,並在大腸鏡的窄頻影像中圈選出息肉所在之區域。本論文所用的分類方法,是透過稀疏矩陣和向量量化編碼,運用於息肉表面紋理特徵的檢測及切割。 本論文所提的方法解決了向量量化的分割區塊選擇的區域特徵的形狀,包括訓練編碼簿的問題。
In recent years, colorectal cancer is the third most common type of cancer worldwide. However, this disease can be prevented by detection and removal of precursor adenomatous polyps during optical colonoscopy (OC). During OC, the endoscopist looks for colon polyps. While hyperplastic polyps are benign lesions, adenomatous polyps are likely to become cancerous. Hence it is common practice to remove all identified polyps and send them to subsequent histological analysis. But removal of hyperplastic polyps poses unnecessary risk to patients and incurs unnecessary costs for histological analysis. In this paper, we develop the first part of a novel optical biopsy application based on narrowband imaging (NBI). A barrier to an automatic system is that polyp classification algorithms require manual segmentations of the polyps, so we automatically segment polyps in colonoscopic NBI data. We propose an algorithm, Classification of Regional Feature (CoRF), which is an extension of the sparse matrix and vector quantization algorithms, a state of the art algorithm for feature detection and segmentation. CoRF solves the intrinsic block selection problem of vector quantization by including training codebook about the shape of the regional feature. CoRF outperforms previous methods with using traditional clustering algorithm--LBG or Kmean clustering algorithms will disperse the energy to other similar block regions, reducing accuracy of analysis in polyps detection.
第一章 導論 1
1.1 研究動機與目的 1
1.2 系統架構 2
1.3 論文架構 3
第二章 相關研究 4
2.1 窄頻影像(Narrow Band Imaging) 4
2.2 約束高斯模糊(Bounded Gaussian) 5
2.3 向量量化編碼(Vector Quantization) 8
2.4 稀疏矩陣(Sparse Matrix) 10
2.5 邊緣偵測(Edge Detection) 11
第三章 偵測診斷窄頻大腸鏡腫瘤系統 12
3.1 影像來源 12
3.2 影像前處理 13
3.2.1 顏色過濾(Chanel Filter ) 13
3.2.2 修正約束高斯模糊(Modify Bounded Gaussian) 14
3.3 紋理特徵抽取及比對 18
3.3.1 向量量化編碼提取及壓縮 18
3.3.2 稀疏表示(Sparse Representation) 20
3.4 診斷結果 21
第四章 結論及未來展望 29
參考文獻 30

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