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研究生:陳宗哲
研究生(外文):Tzong-Jer Chen
論文名稱:應用空間自相關性於醫學影像品質之評估
論文名稱(外文):A spatial autocorrelation function approach to medical image quality evaluation
指導教授:莊 克 士
指導教授(外文):Keh-Shih Chuang
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:2003
畢業學年度:91
語文別:英文
論文頁數:93
中文關鍵詞:影像品質影像壓縮影像品質指標
外文關鍵詞:image qualityimage compressionimage quality indexMoran test
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客觀的影像品質評估方法在各種不同影像處理的應用當中已扮演著重要的角色,本論文之目的即在於建立一種新的、客觀的醫學影像品質評估方法。這個方法主要依據一個名為Moran I 的統計量為基礎。一個較高的Moran Z 值已知其即意味著影像中有較多的結構性反之則有較多的雜訊,而一個經處理後影像例如經壓縮、濾波、去雜訊等處理將照成影像表面像素間相關性的改變,故選擇使用Moran I 統計量去評估影像上空間資訊的變化是正確的,而且選擇不同的使用方式會有不同的結果與應用。原始影像和經處理過的影像的Moran Z值直方圖的峰比值可用作為一個指標用以表示壓縮影像的變化,此指標亦可應用於評估影像平滑的濾器及平滑度。我們發現當應用Moran I統計量於經各種不同方式處理過後的影像時其結果亦與影像退化方式相關。另外,影像的Moran Z值與統計檢定方法結合,可用以證明影像上的微小差異在統計上亦可歸因為一偶發事件,本文證明Moran I統計量在影像品質的評估上唯一有用的工具且可應用於建立多種不同的影像評估方法。
Objective image quality measures play an important task in various image processing applications. The object of this dissertation is to develop new methods for medical image quality measurement. The methods are based on a statistics method, the Moran I statistics. Since a higher Moran Z value means that more structured information exists in the image and random noise is less likely in the image. So, the Moran Z value could be used to represent the spatial properties of mapped data. Image manipulation, such as compression, filtering and denoising, results in a variation of spatial correlation. Using of Moran I statistics to evaluate the variation of spatial information is a good choice. The peak ratio of Moran Z histogram between the manipulated and original images is used as an index for degradation or blurring after image compression or filtering. It can also be as an image blurring and compression index when applied to evaluate images in various compression ratios or filtering methods. A novel quality index is developed by applying Moran I statistics to various processed images and the measured values correlate well with the degree of quality degradation. In addition, the Moran Z value with a statistical test proved that the difference between original and manipulated images for subtle differences in them is attributed to chance. The Moran I statistics is a powerful for measuring image quality and this method can apply to many aspects of image quality evaluation.
Contents
摘要 I
Abstract II
Acknoledgement III
Chapter 1 Introduction 1
1.1 Quality evaluation 1
1.2 Research scope 3
1.2.1 Moran I statistics 4
1.2.2 Application of Moran Z to image quality evaluation 5
1.2.3 Quantification of image blurring 5
1.2.4 Image quality evaluation in compressed image 5
1.2.5 Quality index for medical image 6
1.2.6 Structural information evaluation 6
Chapter 2 Quality Degradation in Lossy Wavelet Image
Compression 7
Abstract 7
2.1 Introduction 8
2.2 Methods 9
2.2.1 Irreversible image compression 9
2.2.2 The Moran test 10
2.2.3 Z-histogram and peak ratio 10
2.2.4 Image data 10
2.3 Results 11
2.4 Discussion and conclusion 12
Chapter 3 Quantification of image blurring using the Moran peak ratio 22
Abstract 22
3.1 Introduction 23
3.2 Methods 24
3.2.1 The Moran test 24
3.2.2 Z-histogram and peak ratio 24
3.3 Experiments and results 25
3.3.1 The average and median filter 25
3.3.2 Ideal low-pass filter 26
3.3.3 High-boost filter 26
3.3.4 Lossy image compression 27
3.4 Discussion 27
3.5 Conclusion 28
Chapter 4 A New Image Quality Index Using Moran I Statistics 40
Abstract 40
4.1 Introduction 41
4.2 Objective Image Quality Measurement 42
4.2.1 Pixelwise error based measurement 42
4.2.2 Q index 42
4.2.3 Moran I test 43
4.3 Results 44
4.4 Discussion and Conclusion 45
Chapter5 A statistical method for evaluation quality of medical images: case study in bit discarding and image compression 51
Abstract 51
5.1 Introduction 52
5.2 Method and Material 54
5.2.1 LSB discarding 54
(a) Whole bit plane method 54
(b) Adaptive method 55
5.2.2 The Z series and distribution 55
5.2.3 The Z distribution for LSB discarding 56
5.2.4 Image compression 56
5.2.5 Image blurring 57
5.2.6 The KS test 57
5.2.7 Images 58
5.3 Results 58
5.3.1 LSBs manipulation 58
5.3.2 Image compression 60
5.4 Discussion 60
5.4.1 Discarding LSBs 60
5.4.2 Electronic phantom 61
5.4.3 Comparison to model observer 62
5.4.4 Effectiveness 63
Chapter 6 Conclusion 85
References 87
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