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研究生：	張逸賢 CHANG, YI-HSIEN
論文名稱：	以多重影像特徵值為基礎之趨勢預測成長法應用於腦迴切割影像分割之研究 The Research of Trend-predicting growing method with Multi Image Feature applied on clipped Gyrus Image Segmentation
指導教授：	陳立祥 CHEN,LI-HSIANG
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電腦與通信工程研究所 Institute of Computer & Communication Engineering
論文出版年：	2009
畢業學年度：	97
語文別：	中文
論文頁數：	117
中文關鍵詞：	模糊、腦迴、黑板系統、影像分割、趨勢預測
外文關鍵詞：	Segment, Fuzzy, Trend-predicting, Gyrus, Blackboard
相關次數：	點閱：114 下載：7
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醫學影像分割是一件頗為複雜的工作，必須結合影像處理、電腦圖學及解剖學等多方面的知識才能加以完成。在本論文中，我們將提出如何利用影像處理的技術，配合專家系統中的黑板架構，將使用者所感興趣區域的輪廓線產生出來，並藉由整合本實驗室另一套3D 立體成像的系統，提供使用者在系統做影像辨識及三維物件結果的展示。
在影像辨識方面，我們將介紹針對腦迴的區域影像所採用的影像處理方法，來幫助我們找出灰質區域正確的輪廓線。希望以灰質輪廓線計算出厚度後，搭配後續建立成3 維物件的操作，正確的輪廓線所產生的資訊才有其參考的價值和意義。影像處理的過程中，使用了全域，區域的門檻法來進行辨識，再搭配多種的影像特徵值作為參考資訊。最後收集影像中確定灰質各個區域的特徵值資訊，並
作趨勢預測，將預測結果利用於區域成長的流程控制。
在黑板架構方面，我們將目前已經完成主體架構的系統，加入操作系統和知識源之間的溝通介面，在進行影像辨識的過程中，能讓醫師可以根據專家知識對系統輸入影像辨識的相關資訊，讓知識源在處理影像辨識時，能有更好的辨識結果。

The segmentation of a medical image is an integrated task. We need to integrate the knowledge of image processing, computer vision and anatomy to complete the task. This thesis describes how to use the techniques of image
processing with a blackboard architecture to generate the contours of the regions of interest. We also integrate another system, 3D Builder, to provide the interface for the users so that we can communicate with our system interactively and view the results of the 3-dimensional reconstruction during the process of recognition.
As far as image processing is concerned, we will describe the segmentation methods for the gray matter of gyrus, for helping us find out the correct regions. We hope use the contours to reconstruct 3D objects and find the thickness of the gray matter area, the information that user operate the gyrus 3D object is meaningful when the gray matter’s contour is correct . On the processing of image recognition, we use the globe
and local threshold method, and use various characteristic value to help segmentation on the algorithm. eventually, for the Trend predicting on region grow, we collect the local characteristic value information of the confirmed gray matter area, and use the predicting result to help use determine the region grow control.
About the blackboard architecture, we add the communication interface between the main system and the knowledge resources in the blackboard system which’s main architecture is already completed. According to professional knowledge, doctors can enter the helpful information into the system in the processes of image recognition.
Then we can gain more correct recognition results with knowledge resources.

§ 中文摘要 § ............................................................... III
§ ABSTRACT § .............................................................. IV
§ 誌謝 § ................................................................... V
§ 目錄 § .................................................................. VI
§ 圖表目錄 § .............................................................. IX
第一章 導論.....................................................................................................................................1
1 概述.......................................................................................................................................1
2 研究動機及目的.....................................................................................................................2
3 章節提要................................................................................................................................4
第二章 研究背景..............................................................................................................................6
1 醫學造影簡介..........................................................................................................................6
2 影像分割與處理分析方法......................................................................................................6
3 三維物件重建系統（3D BUILDER）.......................................................................................9
4 黑板系統架構（BLACKBOARD）.......................................................................................... 12
第三章 三維物件重建系統設計及黑板系統架構更新與設計........................................................ 14
1 架構修正目的....................................................................................................................... 14
2 三維物件重建系統的更新設計............................................................................................ 15
2.1 原有架構設計............................................................................................................... 15
2.2 架構更新....................................................................................................................... 17
3 黑板系統更新設計................................................................................................................ 18
3.1 KS 端原有架構設計....................................................................................................... 18
3.2 IRCore 端原有架構設計................................................................................................ 19
3.3 架構更新....................................................................................................................... 20
4 流程與互動機制................................................................................................................... 22
第四章 針對腦迴灰質影像的分割演算法研究與實作...................................................................24
1 問題定義與特性.................................................................................................................. 24
1.1 以門檻值為基礎的方式Otsu's method.......................................................................... 24
1.2 區域性特徵影像處理....................................................................................................26
1.2.1 Gabor Filter ........................................................................................................................... 26
1.2.2 Non-Linear Diffusion ............................................................................................................. 28
2 以區域性特徵為基礎的演算法設計與測試.......................................................................... 32
2.1 Niblack Algorithm........................................................................................................... 32
2.2 Watershed Algorithm....................................................................................................... 33
2.3 區域遮罩機率調整灰階................................................................................................ 35
2.4 已確定材質區域作區域成長(Region Grow)..................................................................37
3 多尺度形態學區域特徵值的運用......................................................................................... 40
4 人工輔助演算法................................................................................................................... 45
4.1 腦迴脊髓液與灰質交界的路徑操作............................................................................. 45
4.1.1 人工路徑的產生與連接....................................................................................................... 47
4.1.2 以路徑尋找其他二維影像上路徑......................................................................................... 49
4.1.3 以路徑作區域成長................................................................................................................ 59
4.2 流程圖與特徵值選擇....................................................................................................63
5 多個特徵值的綜和資訊利用................................................................................................ 65
5.1 Fuzzy Inference System 於 information fusion 之測試................................................... 66
5.2 特徵距離與空間距離的計算........................................................................................ 71
第五章 趨勢預測區域成長法......................................................................................................... 73
1 REGION GROW ........................................................................................................................ 73
2 以灰值的厚度依據作成長.................................................................................................... 77
3 以趨勢預測決定成長與否.................................................................................................... 82
3.1 趨勢預測資料的收集....................................................................................................84
3.2 Adjusted Exponential Smoothing .................................................................................. 86
3.2.1 Algorithm .............................................................................................................................. 86
3.2.2 αβ參數的決定................................................................................................................... 88
3.2.3 特徵值的選用...................................................................................................................... 89
3.2.4 Result ................................................................................................................................... 90
3.3 Least-squares Method ..................................................................................................... 91
3.3.1 Algorithm .............................................................................................................................. 91
3.3.2 Result ................................................................................................................................... 93
3.4 Kalman.......................................................................................................................... 94
3.4.1 Introduction ........................................................................................................................... 94
3.4.2 多個特徵值的關聯性趨勢預測............................................................................................. 96
3.4.3 單一特徵值變化趨勢預測.................................................................................................... 98
3.4.4 不同尺度的Local Max 特徵值變化趨勢預測..................................................................... 101
3.4.5 以Kalman 趨勢預測系統做區域成長的實驗結果............................................................. 103
第六章 結論................................................................................................................................. 104
1 目前研究成果..................................................................................................................... 104
1.1 系統與演算法研究結果.............................................................................................. 104
1.2 腦迴知識源和操作系統的整合與操作流程................................................................ 105
2 未來發展方向..................................................................................................................... 112
§ 參考文獻 §............................................................................................................................ 114
§ 作者簡介 §............................................................................................................................ 117
                                    

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校內：2014-02-12公開
校外：2014-02-12公開

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