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  1. NTU Theses and Dissertations Repository
  2. 工學院
  3. 應用力學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67916
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor張建成(Chien-Cheng Chang)
dc.contributor.authorYing-Hung Chenen
dc.contributor.author陳盈宏zh_TW
dc.date.accessioned2021-06-17T01:57:54Z-
dc.date.available2025-08-14
dc.date.copyright2020-09-22
dc.date.issued2020
dc.date.submitted2020-08-14
dc.identifier.citation[1] P.-H. Tsui, Z. Zhou, Y.-H. Lin, C.-M. Hung, S.-J. Chung, and Y.-L. Wan, 'Effect of ultrasound frequency on the Nakagami statistics of human liver tissues,' PloS one, vol. 12, no. 8, p. e0181789, 2017.
[2] C. B. Burckhardt, 'Speckle in ultrasound B-mode scans,' IEEE Transactions on Sonics and ultrasonics, vol. 25, no. 1, pp. 1-6, 1978.
[3] P. M. Shankar, 'A general statistical model for ultrasonic backscattering from tissues,' IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 47, no. 3, pp. 727-736, 2000.
[4] P. Shankar, 'Statistical modeling of scattering from biological media,' The Journal of the Acoustical Society of America, vol. 111, no. 5, pp. 2463-2463, 2002.
[5] P.-H. Tsui and C.-C. Chang, 'Imaging local scatterer concentrations by the Nakagami statistical model,' Ultrasound in medicine biology, vol. 33, no. 4, pp. 608-619, 2007.
[6] P.-H. Tsui, C.-C. Huang, C.-C. Chang, S.-H. Wang, and K. K. Shung, 'Feasibility study of using high-frequency ultrasonic Nakagami imaging for characterizing the cataract lens in vitro,' Physics in Medicine Biology, vol. 52, no. 21, p. 6413, 2007.
[7] Y. Y. Liao et al., 'Strain‐compounding technique with ultrasound Nakagami imaging for distinguishing between benign and malignant breast tumors,' Medical physics, vol. 39, no. 5, pp. 2325-2333, 2012.
[8] P.-H. Tsui, C.-K. Yeh, C.-C. Chang, and Y.-Y. Liao, 'Classification of breast masses by ultrasonic Nakagami imaging: a feasibility study,' Physics in Medicine Biology, vol. 53, no. 21, p. 6027, 2008.
[9] P.-H. Tsui, Y.-Y. Liao, C.-C. Chang, W.-H. Kuo, K.-J. Chang, and C.-K. Yeh, 'Classification of benign and malignant breast tumors by 2-D analysis based on contour description and scatterer characterization,' IEEE transactions on medical imaging, vol. 29, no. 2, pp. 513-522, 2010.
[10] P.-H. Tsui et al., 'Ultrasonic Nakagami imaging: a strategy to visualize the scatterer properties of benign and malignant breast tumors,' Ultrasound in medicine biology, vol. 36, no. 2, pp. 209-217, 2010.
[11] P.-H. Tsui, C.-K. Yeh, and C.-C. Chang, 'Microvascular flow estimation by contrast-assisted ultrasound B-scan and statistical parametric images,' IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 3, pp. 360-369, 2009.
[12] M.-C. Ho et al., 'Using ultrasound Nakagami imaging to assess liver fibrosis in rats,' Ultrasonics, vol. 52, no. 2, pp. 215-222, 2012.
[13] M.-C. Ho, Y.-H. Lee, Y.-M. Jeng, C.-N. Chen, K.-J. Chang, and P.-H. Tsui, 'Relationship between ultrasound backscattered statistics and the concentration of fatty droplets in livers: an animal study,' PLoS One, vol. 8, no. 5, p. e63543, 2013.
[14] Y.-H. Lin, C.-C. Huang, and S.-H. Wang, 'Quantitative assessments of burn degree by high-frequency ultrasonic backscattering and statistical model,' Physics in Medicine Biology, vol. 56, no. 3, p. 757, 2011.
[15] P. H. Tsui, C. C. Huang, L. Sun, S. H. Dailey, and K. K. Shung, 'Characterization of lamina propria and vocal muscle in human vocal fold tissue by ultrasound Nakagami imaging,' Medical physics, vol. 38, no. 4, pp. 2019-2026, 2011.
[16] P.-H. Tsui, Y.-L. Wan, and C.-K. Chen, 'Ultrasound imaging of the larynx and vocal folds: recent applications and developments,' Current opinion in otolaryngology head and neck surgery, vol. 20, no. 6, pp. 437-442, 2012.
[17] P. Rangraz, H. Behnam, and J. Tavakkoli, 'Nakagami imaging for detecting thermal lesions induced by high-intensity focused ultrasound in tissue,' Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 228, no. 1, pp. 19-26, 2014.
[18] C. Y. Wang, X. Geng, T. S. Yeh, H. L. Liu, and P. H. Tsui, 'Monitoring radiofrequency ablation with ultrasound Nakagami imaging,' Medical physics, vol. 40, no. 7, p. 072901, 2013.
[19] S. Zhang, C. Li, F. Zhou, M. Wan, and S. Wang, 'Enhanced Lesion‐to‐Bubble Ratio on Ultrasonic Nakagami Imaging for Monitoring of High‐Intensity Focused Ultrasound,' Journal of Ultrasound in Medicine, vol. 33, no. 6, pp. 959-970, 2014.
[20] X. Yang, P. Rossi, D. W. Bruner, S. Tridandapani, J. Shelton, and T. Liu, 'Noninvasive evaluation of vaginal fibrosis following radiotherapy for gynecologic malignancies: A feasibility study with ultrasound B‐mode and Nakagami parameter imaging,' Medical physics, vol. 40, no. 2, p. 022901, 2013.
[21] X. Yang et al., 'Ultrasonic Nakagami‐parameter characterization of parotid‐gland injury following head‐and‐neck radiotherapy: A feasibility study of late toxicity,' Medical physics, vol. 41, no. 2, p. 022903, 2014.
[22] P.-H. Tsui et al., 'Three-dimensional ultrasonic Nakagami imaging for tissue characterization,' Physics in Medicine Biology, vol. 55, no. 19, p. 5849, 2010.
[23] P.-H. Tsui, 'Minimum requirement of artificial noise level for using noise-assisted correlation algorithm to suppress artifacts in ultrasonic Nakagami images,' Ultrasonic imaging, vol. 34, no. 2, pp. 110-124, 2012.
[24] P.-H. Tsui, C.-K. Yeh, and C.-C. Huang, 'Noise-assisted correlation algorithm for suppressing noise-induced artifacts in ultrasonic Nakagami images,' IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 314-322, 2011.
[25] A. Larrue and J. A. Noble, 'Nakagami imaging with small windows,' in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011: IEEE, pp. 887-890.
[26] P.-H. Tsui, H.-Y. Ma, Z. Zhou, M.-C. Ho, and Y.-H. Lee, 'Window-modulated compounding Nakagami imaging for ultrasound tissue characterization,' Ultrasonics, vol. 54, no. 6, pp. 1448-1459, 2014.
[27] T. Higuchi, S. Hirata, T. Yamaguchi, and H. Hachiya, 'Quantitative evaluation of liver fibrosis using multi-Rayleigh model with hypoechoic component,' Japanese Journal of Applied Physics, vol. 52, no. 7S, p. 07HF19, 2013.
[28] T. Higuchi, S. Hirata, T. Yamaguchi, and H. Hachiya, 'Liver tissue characterization for each pixel in ultrasound image using multi-Rayleigh model,' Japanese Journal of Applied Physics, vol. 53, no. 7S, p. 07KF27, 2014.
[29] S. Mori, S. Hirata, H. Hachiya, and T. Yamaguchi, 'Evaluation of fibrotic probability image by multi-Rayleigh model for ultrasound image of liver using automatic region of interest selection,' in 2015 IEEE International Ultrasonics Symposium (IUS), 2015: IEEE, pp. 1-4.
[30] S. Mori, S. Hirata, T. Yamaguchi, and H. Hachiya, 'Probability image of tissue characteristics for liver fibrosis using multi-Rayleigh model with removal of nonspeckle signals,' Japanese Journal of Applied Physics, vol. 54, no. 7S1, p. 07HF20, 2015.
[31] S. Mori, M. Ohashi, S. Hirata, and H. Hachiya, 'Stability evaluation of parameter estimation of multi-Rayleigh model for ultrasound B-mode image of liver fibrosis,' Japanese Journal of Applied Physics, vol. 55, no. 7S1, p. 07KF09, 2016.
[32] K. Tamura, K. Yoshida, H. Maruyama, H. Hachiya, and T. Yamaguchi, 'Proposal of compound amplitude envelope statistical analysis model considering low scatterer concentration,' Japanese Journal of Applied Physics, vol. 57, no. 7S1, p. 07LD19, 2018.
[33] J. Q. Su and J. S. Liu, 'Linear combinations of multiple diagnostic markers,' Journal of the American Statistical Association, vol. 88, no. 424, pp. 1350-1355, 1993.
[34] Y. Liu et al., 'Optimal linear combination of ARFI, transient elastography and APRI for the assessment of fibrosis in chronic hepatitis B,' Liver international, vol. 35, no. 3, pp. 816-825, 2015.
[35] Y. Shen et al., 'Quantitative analysis of non-alcoholic fatty liver in rats via combining multiple ultrasound parameters,' 2019.
[36] Z. Zhou et al., 'Hepatic steatosis assessment with ultrasound small-window entropy imaging,' Ultrasound in medicine biology, vol. 44, no. 7, pp. 1327-1340, 2018.
[37] P.-H. Tsui, M.-C. Ho, D.-I. Tai, Y.-H. Lin, C.-Y. Wang, and H.-Y. Ma, 'Acoustic structure quantification by using ultrasound Nakagami imaging for assessing liver fibrosis,' Scientific reports, vol. 6, no. 1, pp. 1-9, 2016.
[38] T. M. Mitchell, 'Machine learning,' ed: McGraw-hill New York, 1997.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67916-
dc.description.abstract肝臟是沈默的器官,身體發出警訊的時候往往已事態嚴重,且目前的黃金標準-病理切片存在著侵入式診斷的一些缺點,在早期的肝硬化的測量上不是會優先採取的方式,而超音波檢測有即時成像及非侵入式的優點成為臨床醫學上重要的檢測工具。定量式超音波信號診斷方法相比於傳統的灰階影像更能反映不同組織散射子的物理特性,以導入參數成像的方法並分析輸出參數與肝實質病變程度的關係,達到評斷病變程度的目的。
本研究使用雙重Nakagami統計模型分別分析超音波逆散射訊號的隨機干涉,針對病變組織與健康肝細胞的散射訊號振幅分佈差異做出區分,再以不同的參數輸出做成參數影像。Double Nakagami有別於單Nakagami將所有訊號視為一體,將脂滴及正常肝細胞分開計算,再以EM及FM算法找出各分佈的參數做疊合,最後尋找合適的參數輸出與病理切片的分群結果做比較。
本實驗最終在評估脂肪肝中以EM_c算法配合輸出參數μ_F在辨別輕度、中度及重度脂肪肝中分別達到AUC值0.77、0.84、0.84。在高纖維化病人評估脂肪肝的方面以EM算法配合輸出參數μ_F在辨別輕度、中度及重度脂肪肝中分別達到AUC值0.77、0.85、0.84。在評估肝纖維化方面以LC model結合四種參數配合EM_c算法在辨別F1、F2、F3、F4達到AUC值0.71、0.63、0.59、0.64。最後在高脂肪肝病人評估肝纖維化以LC model結合四種參數配合FM_c算法在辨別F1、F2、F3、F4達到AUC值0.93、0.77、0.73、0.81。
zh_TW
dc.description.abstractThe liver is known as a silent organ. When the notable symptoms occur, it turns out to be too late. There are some disadvantages of the current gold standard -liver biopsy. Its’ invasive diagnosis not a preferred method for the evaluation of the early liver cirrhosis. On the contrary, the real-time and non-invasive characteristics of ultrasound imaging made it becomes an important tool in clinical medicine. Compared with the traditional B-mode imaging, the quantitative ultrasonic signal diagnosis method can show the physical characteristics of different backscattered signals. To determine the extent of lesions, this study analyze the relation between the ultrasonic parameters and liver disease by introducing the method of parametric imaging.
Double Nakagami statistical model was used in this study to analyze backscattered signals, distinguish the difference of amplitudes between the backscattered signals of lipid droplets and normal Hepatocytes and make parametric images by output parameters. As opposed to Nakagami consider all signals as one, double Nakagami takes lipid droplets and normal Hepatocytes apart. After that EM and FM algorithms were used to find out the parameters of each distribution. Comparisons are made between the results of liver biopsy.
The results indicate the AUC values of 0.77, 0.84, and 0.84 correspond to the mild, moderate, and severe fatty liver by evaluating the EM_c algorithm and the output parameter for all patients. Upon evaluating the extent to fatty liver of patients with high liver fibrosis, the AUC values of 0.77, 0.85 and 0.84 correspond to the mild, moderate, and severe fatty liver. The evaluation of liver fibrosis for all patients that based on the LC model using four parameters with the EM_c algorithm, resulted in the AUC values of 0.71、0.63、0.59、0.64 correspond to the stage F1、F2、F3、F4. Finally, the evaluation of liver fibrosis with high fatty liver patients that based on the LC model using four parameters with the EM_c algorithm, resulted in the AUC values of 0.93、0.77、0.73、0.81 correspond to the stage F1、F2、F3、F4.
en
dc.description.provenanceMade available in DSpace on 2021-06-17T01:57:54Z (GMT). No. of bitstreams: 1
U0001-1408202016541900.pdf: 4227737 bytes, checksum: 69cfe40e7acc8c5312ff67b06cceab3a (MD5)
Previous issue date: 2020
en
dc.description.tableofcontents致謝 i
中文摘要 ii
Abstract iii
目錄 v
圖索引 viii
表索引 xiii
第一章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 研究目的 4
第二章 理論基礎 5
2.1 Nakagami統計模型 5
2.2 Double Nakagami統計模型 8
2.2.1 EM算法 9
2.2.2 FM算法 17
第三章 實驗方法 18
3.1 病人資訊與收案方式 18
3.2 滑動式窗法 21
3.3 實驗設計 22
3.4 線性組合模型 24
3.5 驗證模型 25
3.5.1 混淆矩陣 25
3.5.2 接受者操作特徵曲線 27
第四章 結果與討論 29
4.1 脂肪肝評估結果 29
4.1.1 EM算法 30
4.1.2 FM算法 34
4.1.3 LC model效果 38
4.1.4 參數影像圖 39
4.2 高纖維化病人脂肪肝評估結果 40
4.2.1 EM算法 41
4.2.2 FM算法 45
4.2.3 LC model效果 49
4.2.4 參數影像圖 50
4.3 肝纖維化評估結果 51
4.3.1 EM算法 52
4.3.2 FM算法 56
4.3.3 LC model效果 60
4.3.4 參數影像圖 63
4.4 高脂肪肝病人肝纖維化評估結果 64
4.4.1 EM算法 65
4.4.2 FM算法 69
4.4.3 LC model效果 73
4.4.4 參數影像圖 74
4.5 討論 75
第五章 結論與未來展望 76
5.1 結論 76
5.2 未來展望 77
dc.language.isozh-TW
dc.title使用雙重Nakagami統計模型進行肝實質病變之超音波評估
zh_TW
dc.titleUltrasound evaluation of liver disease using the double Nakagami distribution
en
dc.typeThesis
dc.date.schoolyear108-2
dc.description.degree碩士
dc.contributor.coadvisor崔博翔(Po-Hsiang Tsui)
dc.contributor.oralexamcommittee朱錦洲(Chin-Chou Chu),林真真(Jen-Jen Lin),陳建甫(Chien-Fu Chen),黃執中(Chih-Chung Huang)
dc.subject.keyword脂肪肝,肝纖維化,超音波參數影像,Double Nakagami,Linear Combination,zh_TW
dc.subject.keywordfatty liver,liver fibrosis,parametric imaging,double Nakagami,linear combination,en
dc.relation.page81
dc.identifier.doi10.6342/NTU202003462
dc.rights.note有償授權
dc.date.accepted2020-08-16
dc.contributor.author-college工學院zh_TW
dc.contributor.author-dept應用力學研究所zh_TW
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