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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹建和 | |
| dc.contributor.author | Chih-Chen Hsueh | en |
| dc.contributor.author | 薛至宸 | zh_TW |
| dc.date.accessioned | 2021-06-08T00:03:40Z | - |
| dc.date.copyright | 2013-08-25 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-14 | |
| dc.identifier.citation | [1] Nicolas D, Nassiri DK, Gaarbutt P, Hill CR. Tissue characterization from ultrasound B-scan data. Ultrasound Med Biol 12:135–143, 1986
[2] Wu C, Chen Y, Hsieh K. Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging 11:141–152,1992 [3] Kadah MY, Farag AA, Zurada MJ, et al. Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE Trans Med Imaging 15:466–478, 1996 [4] Mojsilovic A, Popovic M, Sevic D. Classification of the ultrasound liver images with the 2 x 1-D wavelet transform. IEEE Int Conf Image Processing 1:367–370, 1996 [5] Mojsilovic A, Popovic M, Markovic S, Krstic M. Characterization of visually diffuse diseases from B-scan liver images using non-separable wavelet transform. IEEE Trans Med Imaging 17:541–549, 1998 [6] Ogawa K, Fukushima M, Kubota T, Hisa N. Computer-aided diagnostic system for diffuse liver disease with ultrasonography by neural network. IEEE Trans Nucl Sci 45:3069–3074, 1998 [7] Yeh WC, Li PC, Jeng YM, et al. Elastic modulus measurements of human liver and correlation with pathology. Ultrasound Med Biol 28:467–474, 2002 [8] T. A. Tuthill , R. H. Sperry ,and K. J. Parker , “Deviation from Rayleigh Statistical in Ultrasonic Speckle,” Ultrason. Imag. ,10:81-89, 1988 [9] P.M. Shankar, J.M. Reid, H. Ortega, C.W. Piccoli, and B.B. Goldberg, “Use of Non-Rayleigh Statistics for the Identification of Tumors in Ultrasonic B-Scans of the Breast,” IEEE Trans. on Med. Imag., 12(4):687-692, 1993 [10] V.M. Narayanan, P.M. Shankar, and J.M. Reid, “Non-Rayleigh Statistics of Ultrasonic Backscattered Signals,” IEEE Trans. on Ultrason. Ferr. Freq. Cont., 41(6):845-851, 1994 [11] P.M. Shakar, R. Molthen, V.M. Narayanan, etc. “Studies on the Use of Non-Rayleigh Statistics for Ultrasonic Tissue Characterization,” Ultrason. In Med. Biol., 22(7):873-882, 1996 [12] P.M. Shankar, “A General Statistical Model for Ultrasonic Backscattering from Tissues,” IEEE Trans. on Ultrason. Ferr. Freq. Cont., 47(3):727-736, 2000 [13] P.M. Shankar, V.A. Dumane, J.M. Reid, etc. “Classification of Ultrasonic B-mode Images of Breast Masses Using Nakagami Distribution,” IEEE Trans. on Ultrason. Ferr. Freq. Cont., 48(2):569-580, 2001 [14] P. M. Shankar, 'A compound scattering pdf for the ultrasonic echo envelope and its relationship to K and Nakagami distributions,' IEEE Trans. Ultrason. Ferr. Freq. Cont., 50:339-343, Mar 2003 [15] F. Tranquart, N. Grenier, V. Eder, and L. Pourcelot, “Clinical use of ultrasound tissue harmonic imaging,” Ultrasound Med. Biol. 25:889–894, 1999 [16] J. Thomas and D. Rubin, “Tissue harmonic imaging: Why does it work?,” J. Am. Soc. Echocardiogr. 11:803–808, 1998 [17] Choudhry S, Gorman B, Charboneau JW, Tradup DJ, Beck RJ, Koler JM, Groth DS, Comparison of tissue harmonic imaging with conventional US in abdominal disease. Radiographics 20:1127–113, 2000 [18] HJ Jang, HK Lim, WJ Lee Ultrasonographic evaluation of focal hepatic lesions: comparison of pulse inversion harmonic, tissue harmonic, and conventional imaging techniques J Ultrasound Med, 19:293–299, 2000 [19] P. H. Tsui and S. H. Wang 'The effect of transducer characteristics on the estimation of Nakagami parameter as a function of scatterer concentration', Ultrasound Med. Biol., 30:1345 -1353 2004 [20] P. H. Tsui and C. C. Chang 'Imaging local scatterer concentrations by the Nakagami statistical model', Ultrasound Med. Biol., 33:608 -619 2007 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17261 | - |
| dc.description.abstract | 肝硬化(hepatic cirrhosis)是各種慢性肝病發展的晚期階段。病理上以肝臟彌漫性纖維化、再生結節和假小葉形成為特徵。對於肝臟疾病,超音波檢查往往是首先被用來做影像學診斷的,因為它很簡單,價格低廉,和無侵入性。目前在醫院的超音波部門,組織諧波被廣泛的應用在肝疾病上。相較於傳統基頻超音波,組織諧波對於區域性的肝疾病,像是肝腫瘤、肝癌等,會有明顯的幫助。但對於瀰漫性肝疾病方面,像是肝纖維化、脂肪肝以及肝硬化,相較於傳統基頻超音波,組織諧波影像對於診斷上卻無法證實是否有明顯的幫助。
本論文針對瀰漫性肝疾病中的肝硬化,利用Nakagami統計模型,以定量的方式來偵測肝硬化與正常肝兩者的診斷差別,並藉由合併傳統基頻超音波與組織諧波訊號計算Nakagami m-value,以找出最佳幫助診斷的方式。 | zh_TW |
| dc.description.abstract | The hepatic cirrhosis is the late stage of a variety of chronic liver diseases. Pathologically, it features diffuse liver fibrosis, regenerative nodules, and formation of pseudo lobules. For liver diseases, ultrasound imaging is often the first means to be used for imaging diagnosis, because it is simple, inexpensive, and non-invasive. Currently in the ultrasound department of hospitals, tissue harmonic imaging is widely applied on diagnosis of liver diseases. The tissue harmonic imaging outperforms the traditional fundamental imaging for regional liver diseases, such as liver tumor and liver cancer. But for diffuse liver diseases, such as liver fibrosis, fatty liver, and hepatic cirrhosis, it is not yet proven whether the tissue harmonic imaging can provide obvious diagnostic helps in contrast to the traditional fundamental imaging. .
This thesis focuses on detection of the diffuse liver cirrhosis by employing the Nakagami statistical model to differentiate livers with hepatic cirrhosis from normal livers in a quantitative way., By merging the fundamental and harmonic tissue images to calculate the combined Nakagami m-values, detection rates are significantly improved which has the potential to help clinical diagnosis. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T00:03:40Z (GMT). No. of bitstreams: 1 ntu-102-R99945050-1.pdf: 1812756 bytes, checksum: 668d38d7a30408d70ab0f87e6386ff3d (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii Abstract iii Table of Contents iv List of Figures vii List of Tables ix Chapter 1 序論 1 1.1 前言 1 1.2 研究動機與目的 2 1.3 文獻回顧 3 1.3.1 超音波對於瀰漫性肝疾病上之應用 3 1.3.2 散射子與機率分布統計模型 4 1.3.3 散射子與組織特性 6 1.3.4 組織諧波影像 7 Chapter 2 理論 9 2.1 超音波之散射分析 9 2.1.1 逆散射訊號統計模型 9 2.1.2 Nakagami 統計模型 10 2.2 組織諧波 13 2.2.1 組織諧波原理 14 2.2.2 組織諧波產生機制 15 2.2.3 諧波影像之特性 16 Chapter 3 實驗資料與分析方法 18 3.1 實驗資料 18 3.1.1 仿體實驗 18 3.1.2 臨床資料來源與儀器介紹 18 3.1.3 實驗資料分析方法 20 3.2 超音波在肝硬化上之統計參數 22 3.2.1 肝細胞結構與肝硬化 22 3.2.2 統計參數與肝小葉散射特性的關係 24 3.3 Nakagami m-value 25 3.3.1 選取ROI 25 3.3.2 分區成block 26 3.3.3 m-value計算方法 26 3.3.4 統計流程 28 3.4 基頻與二倍頻合併計算m-value 29 3.4.1 合併計算的m-value關係式 29 3.4.2 模擬訊號測試合併 32 3.4.3 仿體測試合併 34 Chapter 4 分析結果 35 4.1 Nakagami m-value統計結果 35 4.1.1 ROI選取的影響 35 4.1.2 block大小對m-value的影響 38 4.1.3 肝硬化 41 4.1.4 正常肝 43 4.1.5 肝硬化與正常肝的m-value比較 45 4.2 基頻與組織諧波合併計算之m-value 48 4.2.1 方法1: 標準化block平均值 48 4.2.2 方法2: 標準化block標準差 51 Chapter 5 結論與未來工作 53 5.1 結論 53 5.1.1 Nakagami m-value統計結果 53 5.1.2 誤判機率的比較 54 5.2 未來工作 56 Chapter 6 Reference 57 | |
| dc.language.iso | zh-TW | |
| dc.title | 以Nakagami m-參數分析超音波基頻與組織諧波之肝影像 | zh_TW |
| dc.title | Analysis of the Fundamental and Tissue Harmonic Ultrasound Liver Images by Nakagami m-value | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 楊培銘,曹勝凱 | |
| dc.subject.keyword | 組織諧波,肝硬化,Nakagami m-value, | zh_TW |
| dc.subject.keyword | tissue harmonic image,cirrhosis of the liver,Nakagami m-value, | en |
| dc.relation.page | 59 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2013-08-14 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位: | 生醫電子與資訊學研究所 | |
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