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研究生:陳春元
論文名稱:多邊形內插方法在三維影像的研究與應用
論文名稱(外文):Polygon Interpolation for 3-D Image Reconstruction
指導教授:莊克士莊克士引用關係
學位類別:博士
校院名稱:國立清華大學
系所名稱:原子科學系
學門:工程學門
學類:核子工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:68
中文關鍵詞:多邊形內插重建
相關次數:
  • 被引用被引用:0
  • 點閱點閱:220
  • 評分評分:
  • 下載下載:27
  • 收藏至我的研究室書目清單書目收藏:0
摘要
物體3-D表面數據的重建對醫學影像應用非常重要。這些3-D表面的數據可以用來協助視覺、外科手術、放射治療計畫以及診斷。對於兩張來自不同醫療設備影像的對位融合,如將核磁共振造影(MRI)及正子放射斷層造影 (PET) 融合,需將影像資料從新數位化。而影像內插技術即為解決上述問題的好方法。以灰階及形狀為基礎的影像內插法已被廣泛討論與應用,且被發現存在一些不足。在本篇論文中,我們提出一個全新的,以多邊形近似圖形的影像內插方法。多邊形近似圖形的目的主要為用最少的多邊形線段而可以掌握圖形的特性。影像的內插或邊界的重建則以此多邊形的頂點或邊為基礎。在第二章中,我們假設物件邊界的內插即相當於內插近似物件的多邊形。如果用以近似物件的多邊形其頂點可被求出,則邊界可由這些頂點以立體線段( Cubic-spline )內插方式得出。再第三張及第四章中,多邊形被用來近似物件的外型,然後進行內插。對於每一個在目標多邊形( target polygon )中的像素( pixel )值係因其對應到多邊形的邊或頂點的相對位置而決定。對於邊界的內插,我們的方法與形狀為基礎的方法比較後被證明較快且較好。除此以外,多邊形方法的影像品質較優良且與傳統的灰階內插法比較時均方差( mean-squared difference )較小。
Abstract

The reconstruction of a 3-D surface is important in many medical applications. These 3-D surface data can be used to reconstruct the object for visualization, surgery, radiation treatment planning and diagnosis. To register images for different modalities, such as MR and positron emission tomography (PET), these data must be re-digitized. The image interpolation is a useful tool to help solve these problems. Gray-level and shape-based image interpolation methods have been discussed and used extensively. However, these methods bring some drawbacks into existence. In this dissertation, we propose a novel image interpolation method that uses polygon approximation to improving those mentioning methods. Aim for polygon approximation is to capture the essence of the object shape with the fewest possible polygonal segments. Image interpolation or contour construction is then performed based on the polygon vertexes or edges. In Chapter 2, we assume that approximates the object shape. If the polygon vertexes are defined, then the contour can be approximated from these vertexes using a cubic spline interpolation. In Chapters 3 and 4, we use a polygon to approximate the object shape and perform the interpolation. For each pixel inside the target polygon, we determine its relative location in the source slices using the vertices or edges of a polygon as the references, respectively. The target slice gray-level is then interpolated from the corresponding source image pixels. For contour interpolation, our method yields a better contour and is computationally more efficient than shape-based interpolation. In addition, the image quality of this interpolation method is better and the mean squared difference is smaller compared with traditional gray-level image interpolation.
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