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研究生:俞子悠
研究生(外文):Zih-YouYu
論文名稱:以超音波統計參數和影像紋理特徵提取分類肌肉挫傷的復原階段
論文名稱(外文):Classification of Muscle Contusion Healing Stages using Ultrasound Statistical Parameters and Image Texture Features Extraction
指導教授:王士豪王士豪引用關係
指導教授(外文):Shyh-Hau Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:醫學資訊研究所
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:53
中文關鍵詞:肌肉挫傷積體逆散射Nakagami參數灰階共生矩陣支援向量機
外文關鍵詞:muscle contusionintegrated backscatterNakagami parametergray-level co-occurrence matrixsupport vector machine
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挫傷是一種常見的肌肉損傷,是由於在運動中受到瞬間強烈的壓力所造成。目前挫傷可以藉由醫學影像應用於臨床上,但是需要由經驗豐富的專家進行診斷。因此在本研究中,希望透過獲取超音波的射頻 (RF)訊號來計算定量參數和紋理特徵,並加以用於評估挫傷後肌肉組織的恢復情形。實驗使用600g的砝碼從30cm高處墜落撞擊造成大鼠外側腓腸肌的挫傷。以及使用30MHz中心頻率的探頭掃描獲得超音波訊號後,將訊號轉為B-mode影像並計算其積體逆散射 (IB)、Nakagami統計參數 (m)和利用灰階共生矩陣 (GLCM)提取的紋理特徵。最後,使用支援向量機 (SVM)分類器透過超音波影像的定量參數和紋理特徵對挫傷的恢復階段進行分類。 其中IB和m的變化可以清楚地區分破壞階段和其他階段 (p〈0.05)。 而GLCM中的對比度 (CON),同質性 (HOM),能量 (EN)和熵 (ENT)能夠定量地區分肌肉挫傷癒合狀態的各個階段 (p〈0.05)。肌肉挫傷後第21天,由於瘢痕消失不完全以及肌肉再生,導致IB、CON、HOM、EN和ENT這些數值會隨著肌肉恢復而增加,但其數值仍然沒有恢復到健康肌肉的值。同時會透過Masson’s Trichrome染色切片來驗證肌肉的健康狀況。該研究驗證了使用定量參數以及GLCM紋理特徵來判斷肌肉挫傷復原階段為可行的非侵入式診斷方法。基於定量參數和4種Haralick的參數,支援向量機對超音波大鼠外側腓腸肌影像分類已經成功實施。
Contusion is a common injury corresponding to rapid and strong compressive force during exercise occasions. The contusion may be diagnosed clinically via the evaluation of medical images that however require the diagnosis by experienced and professional experts. In the present study, efforts were made to extensively assess the recovery of muscle tissues, which were brought out following a certain contusion hit by a custom-made contusion model, via quantitative parameters and texture feature of the acquired ultrasound radio-frequency (RF) signals and images. The experiments were performed from lateral gastrocnemius muscle of SD rats in which the contusion was achieved by a hitting of 600g mass from a height of 30cm. The integrated backscatter (IB), statistical Nakagami parameter, and texture features extraction using gray-level co-occurrence matrix (GLCM) were calculated from the acquired ultrasound signals and B-mode images with a 30 MHz central frequency. Finally, support vector machine (SVM) classifier was used to classify the contusion recovery stages through quantitative parameters and texture features from ultrasound images. The change of m and IB values, which can clearly differentiate between destruction stages and other stages (p〈0.05). Contrast (CON), Homogeneity (HOM), Energy (EN), and Entropy (ENT) in GLCM are all capable of quantitatively differentiating various muscle contusion healing stages (p〈0.05). 21 days after muscle contusion, the scars dissipated incompletely and muscle regenerates. Specifically, IB, CON, HOM, EN, and ENT tend to increase with the muscle recovery although those values still did not recover to those of the control group as verified by Masson’s Trichrome slices. This study validated that high-frequency ultrasound and related parameters as well as GLCM texture features may be a feasible diagnostic means for quantitatively characterize the degree and recovery of muscle contusion. The efficacy of support vector machine for characterization of ultrasonic rat hind limb images based on quantitative parameters and GLCM with 4 Haralick features has been successfully evaluated.
摘要 I
ABSTRACT II
致謝 IV
TABLE OF CONTENTS V
LIST OF TABLES VII
LIST OF FIGURES VIII
LIST OF ABBREVIATIONS X
CHAPTER 1: INTRODUCTION 1
1.1 General 1
1.1.1 High-Frequency Ultrasound 1
1.1.2 Quantitative Parameters 2
1.1.3 Statistical Models 2
1.1.4 Features Extraction using Gray Level Co-occurrence Matrix 3
1.2 Background 4
1.2.1 Muscle Contusion 4
1.2.2 Contusion Healing Process 5
1.3 Related Research 6
1.4 Motivations and Objectives 7
CHAPTER 2: THEORETICAL BACKGROUND 8
2.1 Fundamentals of Ultrasound Wave Propagation 8
2.2 Reflection and Refraction 9
2.3 Ultrasonic Attenuaiotn and Absorption 10
2.4 Ultrasonic Scattering 11
2.5 Statistical Models for Ultrasonic Backscattered Signals 13
CHAPTER 3: MATERIALS AND METHODS 15
3.1 In Vivo Experiments on Animals 15
3.2 Contusion Model 16
3.3 Experimental Arrangement 17
3.4 Off-line Signal and Image Processing 20
3.4.1 Quantitative Assessments 23
3.4.2 Image Features Extraction 24
3.4.3 Classification of Contusion Healing Stages 26
3.7 Histological Analysis 28
CHAPTER 4: RESULTS 32
4.1 Analysis of Statistical parameters and Texture Features 32
4.2 Classification of Contusion Healing Stages 42
4.3 Histological Sections of Rat Gastronemius Tissue 43
CHAPTER 5: DISCUSSION 44
5.1 Analysis of Statistical parameters and Texture Features 44
5.2 Classification of Contusion Healing Stages 45
5.3 Histological Analysis 46
CHAPTER 6: CONCLUSIONS AND FUTURE WORKS 47
6.1 Conclusions 47
6.2 Future Works 48
REFERENCES 49
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