系統識別號 U0026-0109201713390500
論文名稱(中文) 應用於肌腱醫學影像的分割、追蹤與評估
論文名稱(英文) Segmentation, Tracking, and Evaluation in Tendon Medical Images
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 105
學期 2
出版年 106
研究生(中文) 莊柏逸
研究生(英文) Bo-I Chuang
學號 P78971359
學位類別 博士
語文別 英文
論文頁數 93頁
口試委員 指導教授-孫永年
中文關鍵字 肌腱  超音波影像  顯微影像  分割  追蹤  主動形狀模型  光流向演算法  區塊匹配法 
英文關鍵字 Tendon  Ultrasound  Microscopy  Segmentation  Tracking  Active Shape Model  Optical Flow  Block Matching 
中文摘要 近年來,肌腱病變成為常見的臨床議題。超音波影像及H&E染色顯微影像常用來診斷肌腱病變的嚴重程度。然而由於超音波成像的斑點雜訊,使得傳統的影像處理方法無法穩定的分割及追蹤超音波影像中的肌腱。此外,肌腱運動過程的出平面問題,使得肌腱追蹤更為困難。而在顯微影像部份,在大多數研究中皆透過人工判定來評估肌腱病變嚴重程度,對於客觀的量化分析較為缺乏。
針對上述問題,在本論文中,我們分別在超音波及顯微影像中,提出了三個分割及追蹤的方法。在橫切超音波影像中,我們提出了用以分割肌腱及滑液腔內壁的ATASM。我們設計了適應性權重方法針對ASM能量函式中各能量項在不同位置的重要性給予不同權重值。並對ATASM分割出的區域以小波轉換及共生紋理特徵區分正常與病變影像。在縱切超音波影像中,我們提出了結合光流法及區塊匹配法的OFTB-MKBM。在此方法中,我們對每個相鄰時間點的影像以光流法計算並累計肌腱移動量。再利用累計的移動量選擇合適時間間隔,以多區塊匹配法計算更正確的移動量,最後透過光流法的結果內插時間間隔內的運動量。而在顯微影像中,我們分別對巨觀及微觀影像設計自動化分割方法。在微觀顯微影像中,我們先使用以抽樣為主的閥值法分割細胞核,再依分割出的細胞核數量決定是否要用Laplacian閥值法進一步修正分割結果。在巨觀影像中,我們首先以顏色資訊將血管及鈣化區域分割出,再使用顏色飽和度資訊區分正常與不正常組織。最後再比較微觀與巨觀影像的結果,探討兩者的相關性。在實驗部份,相較於傳統主動輪廓模型(active contour model)及主動形狀模型(active shape model)的結果,我們的ATASM分割出的輪廓與專家圈選的更為接近。並且在42組病人與正常人的影像中,我們的方法能區分出其肌腱病變與否。在OFTB-MKBM方法中,我們使用大體進行驗證,絕對平均誤差在0.05公釐以內。我們也將OFTB-MKBM方法用於假體及病人影像中,並與光流向演算法(optical flow)及多核心區塊匹配法(multi-kernel block matching)比較,並得到更準確的追蹤結果。在顯微影像部份,我們使用專家判斷的結果驗證微觀影像分割方法,並得到90.85%的敏感度性,優於使用最大亂度閥值法(maximum entropy thresholding)及水平集分割法(level set)的分割結果。我們也驗證微觀與巨觀影像的分割結果,從實驗結果顯示兩分割結果有很好的相關性。
英文摘要 Tendinopathy is a common clinical issue in recent years. Ultrasound and H&E stained tendon microscopy images are commonly used for the clinical diagnosis of tendinopathy severity. Due to property variations of ultrasound images, traditional methods cannot effectively segment the finger joint’s tendon structure. Moreover, speckle noise and out-of-plane issues make the tendon tracking process difficult. In microscopy, as most of the reported tendon tissue evaluations are manual, objective quantitative analyses remain scant.
In this thesis, we developed three tracking and segmentation methods for ultrasound and microscopy images. In axial view ultrasound images, an adaptive texture-based active shape model (ATASM) method is proposed for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area., In order to automatically track the tendon motion, we developed a new method called optical-flow-trend-based multi-kernel block matching (OFTB-MKBM) that combines the advantages of optical flow and multi-kernel block matching in the sagittal view ultrasound images. For every pair of adjacent image frames, the optical flow is computed and used to estimate the accumulated displacement. The proposed method selects the frame interval adaptively based on this displacement. Multi-kernel block matching is then computed on the two selected frames. To reduce the tracking errors, the detailed displacements of the frames in between are interpolated based on the optical flow results. In microscopy, we develop two automatic systems for tendon nuclei segmentation from micro- and macro-view images. In micro-view microscopy, the proposed sampling-based thresholding segments the nuclei by feature sampling from a small number of selected nuclei. For complex images with more nuclei, the Laplacian-based thresholding is proposed to segment the nuclei based on the nuclei boundary information. The system selects the thresholding result depending on the number of segmented nuclei. The segmented nuclei are then classified as normal or abnormal based on their characteristics. In macro-view microscopy, we first segment the vessel and calcified regions according to the color information. The remaining regions are then classified into normal or abnormal tissues based on their saturation values. In the experiments, the results of proposed ATASM had fewer errors, with respect to the ground truth, than the traditional active contour model (ACM) and active shape model (ASM). The segmentation results were all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases were also a good reference for diagnosis according to the expert clinicians’ opinions. The mean absolute error of OFTB-MKBM evaluated by using cadaver data was less than 0.05 mm. The proposed OFTB-MKBM also tracked the motion of phantom and tendons in vivo, and achieved the better tracking results than optical flow and multi-kernel block matching. The average sensitivity of micro-view segmentation method in the microscopy achieved 90.85% with respect to expert judgments. The results are better than the ones using maximum entropy threshold and level set segmentation structure. The experiment also showed that the proposed micro- and macro-view classifications have good correlation with each other.
1.1 Motivation 1
1.2 Outlines 9
CHAPTER 2 Tendon Segmentation in Ultrasound Images 11
2.1 ATASM Overview 11
2.2 Shape Model Construction 13
2.3 Texture Profile Construction 13
2.4 Energy Function Setup 15
2.5 Weight of Energy Term Computation 16
2.6 Shape Model Locating 17
2.7 GA-Based Energy Optimization 17
2.8 ATASM for Tendon Segmentation 18
2.9 ATASM for Synovial Sheath Segmentation 21
2.10 Synovial Sheath Segmentation Post-processing 22
3.1 Optical flow method 23
3.2 Multi-kernel block matching 24
3.3 Optical-flow-trend-based multi-kernel block matching 24
4.1 Micro-view Segmentation & Classification 31
4.2 Macro-view segmentation 36
4.3 Correspondence between micro- and macro-view images 38
5.1 Results of axial view ultrasound segmentation 41
5.2 Results of sagittal view ultrasound tracking 54
5.3 Results of micro- and macro-view microscopy image 70
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