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系統識別號 U0026-0812200915212728
論文名稱(中文) 以硬體實現小波頻帶能量擷取應用於超音波乳房腫瘤影像特徵描述
論文名稱(英文) A Novel Hardware Implementation of Wavelet-based Octave Energy for Breast Sonogram Tumor Characterization
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 97
學期 2
出版年 98
研究生(中文) 李協衛
研究生(英文) Hsieh-Wei Lee
學號 n2892146
學位類別 博士
語文別 英文
論文頁數 87頁
口試委員 召集委員-謝文雄
指導教授-劉濱達
口試委員-黃有榕
口試委員-廖斌毅
口試委員-詹寶珠
口試委員-鄭國順
指導教授-洪金車
中文關鍵字 區段累加演算法  非遞迴式小波轉換  小波頻帶能量  超音波影像  乳房腫瘤辨識 
英文關鍵字 Wavelet-Based Octave Energy  Ultrasound Images  Breast Cancer Classification  Segment Accumulation Algorithm (SAA)  Reversible Round-off 1-D Non-Recursive DPWT 
學科別分類
中文摘要 乳房腫瘤輪廓的規則性是辨別腫瘤良惡性一個很重要的特徵。對電腦輔助診斷系統而言,如何用特徵參數來代表乳房腫瘤輪廓規則性是一個關鍵的課題。在我們的研究中,乳房腫瘤輪廓規則性可以視為一維訊號中的區域變異性。而區域變異性可以藉由一維離散週期小波轉換的高階轉換係數能量求得。為了減少運算複雜度,我們使用可逆的一維離散非遞迴式小波轉換來計算高階轉換係數。
特徵參數必需能抗雜訊且不會因乳房腫瘤輪廓變動而影響診斷效能。為了評估乳房腫瘤輪廓變動對診斷效能以及特徵值所造成的影響,此研究使用的樣本由兩種方法產生,分別為人工手繪腫瘤輪廓以及使用ImageJ影像處理程式設定三組不同的參數繪製。實驗結果顯示,我們提出高階轉換係數能量診斷效能和專業醫師的診斷非常接近。值得注意的是,分別以三組ImageJ影像處理程式產生的腫瘤輪廓為實驗樣本所得到的ROC參數分析數據差異並不明顯。另外,結合其他和輪廓相關的特徵參數可以進一步獲得很大的診斷效率改善。
在硬體方面,本論文使用區段累加演算法(SAA)來實現高階轉換係數,此種演算法可以大量地減少乘法器和加法器的數目,而且不會造成系統時間延誤。另外,本論文亦進一步探討有效位元對診斷效能以及硬體資源的影響。
英文摘要 The infiltrative nature of lesions is a significant feature that implies a malignant breast lesion in ultrasound images. Characterizing the infiltrative nature of lesions with computationally inexpensive and highly efficacious features is crucial for the realization of a computer-aided diagnosis system. In this study, the infiltrative nature of lesions is regarded as an energy that produces irregular and considerably local variances in a 1-D signal. The local variances can be characterized by a few high octave energies (i.e., the channel energies close to low frequency bands) in 1-D discrete periodized wavelet transform (DPWT). To reduce computation cost, high octave decomposition is performed by a reversible round-off 1-D non-recursive DPWT (1-D RRO-NRDPWT).
For breast lesion classification, contour features should be able to resist noise and contour variation. To evaluate the differences in performance and feature value induced by the variations in boundary definitions, two delineation methods were considered, hand-painted and semi-automatic segmentation. A high individual performance result implies that the proposed feature is well correlated with radiologist’s perception and closer to match those in trained physician than morphometric parameters. The performance differences in the three ImageJ-generated datasets derived by variant setting parameters are not significant. Experimental results also reveal that the proposed feature is suitable for combination with some morphometric parameters for performance improvement.
For the realization of high octave decomposition, a segment accumulation algorithm (SAA) is also presented in this dissertation. The SAA is a new folding technique that can reduce multipliers and adders dramatically without the cost of increasing latency. Finite precision performance analysis is also taken to study the word length suppression efficiency and the feature efficacy in breast lesion classification on ultrasonic images.
論文目次 Abstract i
List of Tables viii
List of Figures x

CHAPTER 1 Introduction 1
1.1 Background and Motivation 1
1.2 Organization of the Dissertation 5

CHAPTER 2 The Reversible Round-Off Non-Recursive 1-D Discrete Periodized Wavelet Transform 6
2.1 Exploration of Word-Length-Growth Effect. 7
2.2 The Non-Recursive 1-D Discrete Periodized Wavelet Transform 7
2.3 The Reversible Round-Off 1-D NRDPWT Theorem 12
2.4 Finite Precision Performance of the Reversible Round-Off 1-D NRDPWT 19

CHAPTER 3 Octave Energy Features for Breast Lesion Description 23
3.1 Breast US Images Acquisition 24
3.2 Derivation of Breast Lesion Contour 24
3.3 Resampling Process 29
3.4 Multiresolution Representations of Breast Lesion Shapes 30
3.5 Measurement of Fisher’s Estimation 33

CHAPTER 4 Experimental Results of Classification Performance Analysis 35
4.1 Materials and Methods 35
4.2 Experimental Results 36
4.2.1 Individual performance of morphometric and octave energy features by using manual delineated breast lesions 36
4.2.2 Combined performance of morphometric and octave energy features by using manual delineated breast lesions 39
4.2.3 Performance of morphometric and octave energy features by using semi-automatic delineated breast lesions 42


CHAPTER 5 Segment Accumulation Algorithm for the Realization of High Octave Decomposition 50
5.1 The Segment Accumulation Algorithm 50
5.2 Example 53

CHAPTER 6 SAA-Based VLSI Architecture for High Octave Decomposition 56
6.1 VLSI Architecture Design of Segment Accumulation Algorithm 56
6.2 Bit Performance Analysis of NRDPWT Filter Coefficients 60
6.3 Hardware Simulation Results 67

CHAPTER 7 Discussions and Conclusions 72

References 77
Appendix 83
Publication List 87
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