博碩士論文 107221010 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:21 、訪客IP:18.215.185.97
姓名 邱繼賢(Chi-Hsien Chiu)  查詢紙本館藏   畢業系所 數學系
論文名稱
(Early Detection of Mouse Liver Fibrosis Using Segmentation of MR Images with Confusion Component Removing and Morphology)
相關論文
★ 非線性塊狀高斯消去牛頓演算法在噴嘴流體的應用★ 以平行 Newton-Krylov-Schwarz 演算法解 Poisson-Boltzmann 方程式的有限元素解在膠體科學上的應用
★ 最小平方有限元素法求解對流擴散方程以及使用Bubble函數的改良★ Bifurcation Analysis of Incompressible Sudden Expansion Flows Using Parallel Computing
★ Parallel Jacobi-Davidson Algorithms and Software Developments for Polynomial Eigenvalue Problems in Quantum Dot Simulation★ An Inexact Newton Method for Drift-DiffusionModel in Semiconductor Device Simulations
★ Numerical Simulation of Three-dimensional Blood Flows in Arteries Using Domain Decomposition Based Scientific Software Packages in Parallel Computers★ A Parallel Fully Coupled Implicit Domain Decomposition Method for the Stabilized Finite Element Solution of Three-dimensional Unsteady Incompressible Navier-Stokes Equations
★ A Study for Linear Stability Analysis of Incompressible Flows on Parallel Computers★ Parallel Computation of Acoustic Eigenvalue Problems Using a Polynomial Jacobi-Davidson Method
★ Numerical Study of Algebraic Multigrid Methods for Solving Linear/Nonlinear Elliptic Problems on Sequential and Parallel Computers★ A Parallel Multilevel Semi-implicit Scheme of Fluid Modeling for Numerical Low-Temperature Plasma Simulation
★ Performance Comparison of Two PETSc-based Eigensolvers for Quadratic PDE Problems★ A Parallel Two-level Polynomial Jacobi-Davidson Algorithm for Large Sparse Dissipative Acoustic Eigenvalue Problems
★ A Full Space Lagrange-Newton-Krylov Algorithm for Minimum Time Trajectory Optimization★ Parallel Two-level Patient-specific Numerical Simulation of Three-dimensional Rheological Blood Flows in Branching Arteries
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2021-8-31以後開放)
摘要(中) 慢性肝病常會進行到不可逆的肝硬化階段,但是在肝纖維化的早期,其病勢是可逆的。
雖然侵入式診斷是目前評斷肝纖維化的黃金標準,除了成本較為高昂且會造成病人的
痛苦且帶有些微機率的後遺症。所以我們希望能建立一套簡單但可靠的非侵入式評分
方法,並期望能判別患者肝纖維化的程度。主要影像是使用小鼠的MR 影像,利用
MR 影像的T1 和T1ct (Primovist) 各自的成像差異加上K-means 法把肝影像分割出來。我們架構的模型主要使用Support vector machine (SVM) 去進行分類。透過觀察,我們使用simple connected domain 的機率當作其中的一個特徵,並用孔隙率當作第二個特徵。由於SVM 本身是Binary 分類器,所以我們選用One-Against-One 策略,使用多個Binary 分類器去達到我們想要的分類效果。由於資料組數較少,因此我們選用K-Fold 交錯驗證法來增強結果的可信度。單獨使用孔隙率當作特徵所得到的模型準確率大約有80%,根據ROC 曲線分析,可以得出其AUROC 分數大約為0.7 ∼ 0.8 之間,但如果加上simple connected domain 作為第二特徵,訓練出來的模型準確率可達 90%,AUROC 分數可達將近0.9,代表其判斷結果是可信的。
摘要(英) Chronic liver disease often progresses to the irreversible stage of cirrhosis, but in the early
stage of liver fibrosis, its disease is reversible. Although the invasive diagnosis is currently
the gold standard for judging liver fibrosis, in addition to the high cost and the painful
sequelae of the patient with some micro-probability. Therefore, we hope to establish a
simple but reliable non-invasive scoring method and hope to determine the degree of liver
fibrosis in patients. The main image is the MR image of the mouse. The liver image is
segmented using the respective imaging differences of T1 and T1ct (Primovist) of MRI
plus the K-means method. The model of our architecture mainly uses a support vector
machine (SVM) to classify. Through our observation, we use the degree of the simple
connected domain as one of the features, and the porosity as the second feature. Since
SVM is a binary classifier, we choose the one-against-one mode and use multiple binary
classifiers to achieve the classification effect we want. Due to the small number of data
sets, we choose K-Fold cross-validation to enhance the credibility of the results. The
accuracy of the model obtained by using porosity alone as a feature is only about 80%.
According to the ROC curve analysis, it can be concluded that its AUROC score is about
0.7∼0.8, but if the simple connected domain is added as the second feature, the accuracy
of the trained model can reach 90%, and the AUROC score can reach close to 0.9, which means that the judgment result is credible.
關鍵字(中) ★ 肝纖維化 關鍵字(英) ★ Liver Fibrosis
論文目次 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Segmented morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 Images segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Methodology of Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2.1 Basic Morphological Operators . . . . . . . . . . . . . . . . . . . . 5
3 Proposed diagnosis technique . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1 Feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1.1 Feature 1 : Simple connected domain . . . . . . . . . . . . . . . . 10
3.1.2 Feature 2 : Porosity . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Cross validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Classification Method : Support Vector Machine (SVM) . . . . . . . . . . 16
3.3.1 One-Against-One (OAO) . . . . . . . . . . . . . . . . . . . . . . . . 17
4 Diagnosis results and discussions . . . . . . . . . . . . . . . . . . . . . . . . 19
4.1 Data information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Diagnosis processed result . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3 Feature 1 & Feature 2 results . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3.1 Feature 1 : Simple connected domain . . . . . . . . . . . . . . . . . 21
4.3.2 Feature 2 : Porosity . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.4 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.4.1 ROC Curve & AUROC Analysis . . . . . . . . . . . . . . . . . . . 26
5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
參考文獻 [1] J. Axnick. β1 integrin-dependent mechanotransduction induces angiocrine signaling
required to promote liver growth and survival. PhD thesis, 2019.
[2] J.W. Chen. Exploring effects of optimizer selection and their hyperparameter tuning
on performance of deep neural networks for image recognition. Master’s thesis,
National Central University, 2019.
[3] U.R. Kamalov A.V. Kvostikov, A.S. Krylov. Ultrasound image texture analysis for
liver fibrosis stage diagnostics. Programming and Computer Software, 41:273–278,
2015.
[4] G.N Hounsfield. Computerized transverse axial scanning (tomography): Part 1.
description of system. The British Journal of Radiology, 46:1016–1022, 1973.
[5] A. Kumar, D. Welti, and R.P Ernst. Imaging of macroscopic objects by NMR Fourier
zeugmatography. Naturwissenschaften, 62:34–34, 1975.
[6] S.C. Faria, K. Ganesan, I. Mwangi, M. Shiehmorteza, B. Viamonte, S. Mazhar,
M. Peterson, Y. Kono, C. Santillan, G. Casola, et al. MR imaging of liver fibrosis:
current state of the art. Radiographics, 29:1615–1635, 2009.
[7] L. Petitclerc, G. Gilbert, B.N. Nguyen, and A. Tang. Liver fibrosis quantification by
magnetic resonance imaging. Topics in Magnetic Resonance Imaging, 26:229, 2017.
[8] K. Sandrasegaran, F.M. Akisik, C. Lin, B. Tahir, J. Rajan, R. Saxena, and A.M.
Aisen. Value of diffusion-weighted MRI for assessing liver fibrosis and cirrhosis.
American Journal of Roentgenology, 193:1556–1560, 2009.
[9] T.L. Harada, K. Saito, Y. Araki, J. Matsubayashi, T. Nagao, K. Sugimoto, and
K. Tokuuye. Prediction of high-stage liver fibrosis using adc value on diffusionweighted
imaging and quantitative enhancement ratio at the hepatobiliary phase of
gd-eob-dtpa–enhanced MRI at 1.5 t. Acta Radiologica, 59:509–516, 2018.
[10] C.H. Thng and D.J. .and Koh D.M. San Koh, T.and Collins. Perfusion magnetic
resonance imaging of the liver. World Journal of Gastroenterology: WJG, 16:1598,
2010.
[11] M. Yin, K.J. Glaser, J.A. Talwalkar, J. Chen, A. Manduca, and R.L. Ehman. Hepatic
MR elastography: clinical performance in a series of 1377 consecutive examinations.
Radiology, 278:114–124, 2016.
[12] H. Morisaka, U. Motosugi, S. Ichikawa, T. Nakazawa, T. Kondo, S. Funayama,
M. Matsuda, T. Ichikawa, and H. Onishi. Magnetic resonance elastography is as
accurate as liver biopsy for liver fibrosis staging. Journal of Magnetic Resonance
Imaging, 47:1268–1275, 2018.
[13] T. Yokoo, T. Wolfson, K. Iwaisako, M.R. Peterson, H. Mani, Z. Goodman,
C. Changchien, M.S. Middleton, A.C. Gamst, S.M. Mazhar, et al. Evaluation of
liver fibrosis using texture analysis on combined-contrast-enhanced magnetic resonance
images at 3.0 t. BioMed research international, 2015.
[14] Y. Yu, J. Wang, C.W. Ng, Y. Ma, S. Mo, E.L.S. Fong, J. Xing, Z. Song, Y. Xie,
K. Si, et al. Deep learning enables automated scoring of liver fibrosis stages. Scientific
reports, 8:1–10, 2018.
[15] T.H. Kim, J.E. Kim, J.H. Ryu, and C.W. Jeong. Development of liver surface
nodularity quantification program and its clinical application in nonalcoholic fatty
liver disease. Scientific reportz, 9:1–10, 2019.
[16] W.G. Bradley D.D. Stark. Magnetic resonance imaging; the cv mosby company: St.
Louis, Washington DC, Toronto, pages 161–181, 1988.
[17] A.D. McLachlan A. Carrington. Introduction to magnetic resonance: with applications
to chemistry and chemical physics. 1967.
[18] Y.T. Juan. Three-dimensional geometry reconstruction of mouse liver from MR
images using k-means method with confusion component removing. Master’s thesis,
National Central University, 2019.
[19] M. Singh, P. Patel, D. Khosla, and T. Kim. Segmentation of functional MRI by
k-means clustering. IEEE Transactions on Nuclear Science, 43:2030–2036, 1996.
[20] H.P. Ng, S.H. Ong, K.W.C. Foong, P.S. Goh, and W.L. Nowinski. Medical image
segmentation using k-means clustering and improved watershed algorithm. In 2006
IEEE southwest symposium on image analysis and interpretation, pages 61–65. IEEE,
2006.
[21] K.S. Sindhushree, T.R. Manjula, and K. Ramesha. Detection and 3d reconstruction
of brain tumor from brain MR images. International Journal of Engineering Research
& Technology (IJERT), 2:528–534, 2013.
[22] R.C. Gonzalez, R.E. Woods, and S.L. Eddins. Digital image processing using MATLAB.
Pearson Education India, 2004.
[23] P. Refaeilzadeh, L. Tang, and H. Liu. Cross-validation. Encyclopedia of database
systems, 5:532–538, 2009.
[24] J.D. Rodriguez, A. Perez, and J.A. Lozano. Sensitivity analysis of k-fold cross validation
in prediction error estimation. IEEE transactions on pattern analysis and
machine intelligence, 32:569–575, 2009.
30
指導教授 黃楓南(Feng-Nan Hwang) 審核日期 2020-8-19
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明