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研究生:謝執中
研究生(外文):Chih-Chung Hsieh
論文名稱:結合紋理特徵分析與模糊可能性分群分割腦部磁振影像
論文名稱(外文):Texture feature analysis with fuzzy possibilistic c-means for brain MR image segmentation
指導教授:張恆華
口試委員:丁肇隆李佳翰張瑞益
口試日期:2015-07-17
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:工程科學及海洋工程學研究所
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:2015
畢業學年度:103
語文別:中文
論文頁數:109
中文關鍵詞:頭骨去除紋理特徵影像分割腦部田村紋理特徵灰階共生矩陣灰階連續長度矩陣模糊可能性分析
外文關鍵詞:Skull-strippingtexture-featureimage segmentationbrainTamura texture featureGLCMGLRLMfuzzy possibilistic c-means
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腦部磁振影像分割,又名大腦擷取或頭骨去除(Skull Stripping),是醫學影像分析的重要前處理之一。因為人類腦部的高複雜度和磁振造影(Magnetic Resonance Imaging,MRI)的多變參數影響,頭骨去除是具有相當挑戰性的。在本篇論文當中,我們提出一個利用區域紋理特徵與模糊可能性分析結合型態學的演算法。在所提出的演算法中,我們對影像每一點計算區域特徵值,分別為田村(Tamura)紋理特徵、灰階共生矩陣(Gray Level Co-Occurrence Matrix)及灰階長度矩陣(Gray Level Run-Length Matrix)特徵。田村紋理特徵使用粗糙度、對比度、方向性、線相似性、知覺性、規律性以及粗略性來代表影像的特徵值,本篇論文取前三項使用。灰階共生矩陣研究灰階值的空間相關特性,反映灰階值的分布特性,也是種常用的描述紋理方法。灰階長度矩陣紋理特徵則根據角度計算灰階值的連續長度得出特徵矩陣,再計算各種特徵值。接著使用模糊可能性分群的方法來將特徵影像(Feature image)分類。最後使用型態學對分類過後的特徵影像做處理。首先使用侵蝕處理,接著找出最大區域,由於腦接近眼睛的影像常和許多組織混在一起,若是直接找最大區域,所得出的結果將不是我們所需要的,但是腦中間切片卻可以分的十分清楚,因此我們從腦中央開始往兩端做運算,利用腦部是連續的且中間切片一定是最大的概念,取前一張影像結果作遮罩計算影像和遮罩交集的區域。再利用擴張將受侵蝕的影像復原,結尾將影像中的小洞填滿。最後我們將實驗結果與現有的兩種廣為人知的方法做比較,比較結果指出本研究所提出之演算法,在腦部分割影像網路資料庫所提供的臨床真實磁振影像的頭骨去除結果,有更好的精準度。

Segmentation of brain tissue from non-brain tissue, also known as skull stripping, has been challenging due to the complexity of human brain structures and variable parameters of MR scanners. It is one of the most important preprocessing steps in medical image analysis. Skull stripping is often performed using a sequence of mathematical morphological operations following an initial separation of the brain from other tissues of the head. We propose a new brain segmentation algorithm that is based on a texture feature analysis, fuzzy possibilistic c-means and morphological operations. Tamura texture feature consist of six features. Gray Level Run-Length Matrices method is a comparably simple and straightforward texture analysis approach, and So does gray level co-occurrence matrix. Three methods are well-known and representative.
After computation of textures, we apply fuzzy possibilistic c-means(FPCM) for voxel clustering, which provides a labeled image for the following morphological operations.
The last step, we then apply sequence morphological operations followed by FPCM to find out the brain region. Our method starts from middle image to side because of the high accuracy in middle.
We compare our methods with two famous methods, with internet brain segmentation repository data sets. Experimental results indicated that the proposed algorithm is effectively and potential application in a wide variety of brain image segmentation.


致謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 viii
表目錄 xi
符號表 xii
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 論文架構 4
第2章 文獻探討 6
2.1 核磁共振影像 6
2.2 腦表面擷取模型 7
2.2.1 非均向性擴散濾波器 8
2.2.2 邊緣偵測 8
2.2.3 形態學處理 9
2.3 大腦擷取工具 11
2.4 水平集方法 13
第3章 研究設計與方法 15
3.1 基本觀念 15
3.1.1 分群分析 15
3.1.2 紋理分析 18
3.2 演算法流程 19
3.3 非均向性擴散濾波器 20
3.4 影像紋理特徵 21
3.4.1 田村紋理特徵 21
3.4.2 灰階共生矩陣之紋理特徵 25
3.4.3 灰階連續長度矩陣之紋理特徵 29
3.5 影像紋理特徵評估 33
3.6 模糊可能性分群法 38
3.7 形態學處理 41
第4章 實驗結果及討論 45
4.1 實驗說明 45
4.2 IBSR 影像資料庫及LONI Test Data Archive 1.0 47
4.3 結果 48
4.3.1 IBSR第一組影像資料庫 48
4.3.2 IBSR第二組影像資料庫 57
4.3.3 LONI Test Data Archive 1.0 65
第5章 結論與未來展望 72
5.1 結論 72
5.2 未來展望 73
參考文獻 74
附錄一 紋理特徵影像-IBSR 78
一之一 視窗範圍:5X5 78
灰階共生矩陣特徵影像 78
灰階連續長度矩陣特徵影像 79
田村紋理特徵影像 82
一之二 視窗範圍:7X7 83
灰階共生矩陣特徵影像 83
灰階連續長度矩陣特徵影像 84
田村紋理特徵影像 87
一之三 視窗範圍:9X9 87
灰階共生矩陣特徵影像 87
灰階連續長度矩陣特徵影像 89
田村紋理特徵影像 92
一之四 視窗範圍:11X11 92
灰階共生矩陣特徵影像 92
灰階連續長度矩陣特徵影像 93
田村紋理特徵影像 96
附錄二 紋理特徵影像-LONI 97
二之一 視窗範圍:5X5 97
灰階共生矩陣特徵影像 97
灰階連續長度矩陣特徵影像 98
田村紋理特徵影像 100
二之二 視窗範圍:7X7 100
灰階共生矩陣特徵影像 100
灰階連續長度矩陣特徵影像 101
田村紋理特徵影像 103
二之三 視窗範圍:9X9 103
灰階共生矩陣特徵影像 103
灰階連續長度矩陣特徵影像 104
田村紋理特徵影像 106
二之四 視窗範圍:11X11 106
灰階共生矩陣特徵影像 106
灰階連續長度矩陣特徵影像 107
田村紋理特徵影像 109


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