請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23384
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳中明 | |
dc.contributor.author | Che-Wei Chang | en |
dc.contributor.author | 張哲瑋 | zh_TW |
dc.date.accessioned | 2021-06-08T05:00:14Z | - |
dc.date.copyright | 2010-08-19 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-08-16 | |
dc.identifier.citation | [1] Albanes, D., Weinberg, G. B., Boss, L. et al., “A survey of physician’s breast cancer early detection practices,” Prev Med. 1988; 17: 643–52.
[2] Schreer, I., Luttges, J., “Breast cancer: early detection,” Eur Radiol. 2000; 10: 331–38. [3] Rieber, A., Brambs, H. J., Gabelmann, A., et al. “Breast MRI for monitoring response of primary breast cancer to neo-adjuvant chemotherapy,” Eur Radiol. 2002; 12: 1711-1719. [4] Barnes R. B., “Thermography of the Human Body,” Science. 1963; 140: 870. [5] Qi, H., Kuruganti, P. T., Snyder, W. E., “Detecting breast cancer from thermal infrared images by asymmetry analysis,' In Biomedical Engineering Handbook. 2006, CRC Press. [6] Ng, E. Y. K., Ung, L. N., Ng, F. C. et al., “Statistical analysis of healthy and malignant breast thermography,” Journal of Medical Engineering and Technology. 2001; 6: 253–263. [7] Gautherie, M., “Atlas of breast thermography with specific guidelines for examination and interpretation,” Milan, Italy: PAPUSA. 1989. [8] Zylberberg, B., Salat-Baroux, J., Ravina, J. H. et al., “Initial chemoimmunotherapy in inflammatory carcinoma of the breast,” Cancer. 1982; 49: 1537-1543. [9] Boyd, A., Maloney, S., “Digital infrared thermal imaging as biofeedback tool: Monitoring chemotherapy response in a young female with breast cancer mediastinal secondaries,” The Second Joint EMBS-BMES Conference, 2002; 23-26. [10] Szu, H., Miao, L., Qi, H., “Thermodynamic free-energy minimization for unsupervised fusion of dual-color infrared breast images,” Proc. of SPIE. 2006 Apr 17; 6247: 62470-15. [11] Szu, H., Buss, J. R., Kopriva, I., Nonlinear blind demixing of single pixel underlying radiation sources and digital spectrum local thermometer, U.S. Patent No. 0181375 A1, 2004. [12] Schellart, N. A. M.,. Compendium of Medical Physics, Medical Technology and Biophysics. 2008 Jun; 367. [13] Szu, H., Hsu, C., “Blind de-mixing with unknown sources,” International Conference on Neural Networks. 1997 Jun 9-12; 4: 2513-18. [14] Szu, H., Hsu, C., “Landsat spectral demixing a la super resolution of blind matrix inversion by constraint MaxEnt neural nets,” Proc. SPIE. 1997 Apr 22; 3078: 147-60. [15] Ng, E. Y. K., Fok, S. C., Peh, Y. C. et al., “Computerized detection of breast cancer with artificial intelligence and thermograms,” Int. J. Med. Eng. Technol. 2002; 26 (4): 52–157. [16] Ohsumi, S., Takashima, S., Aogi, K. et al., “Prognostic value of thermographical findings in patients with primary breast cancer,”. Breast Cancer Res Treat, 2002; 74 (3): 213-20. [17] Schaefer, G., Zavisek, M., Nakashima, T., “Thermography based breast cancer analysis using statistical features and fuzzy classification,” Pattern Recognit. 2009; 42 (6): 1133–1137. [18] Keyserlingk, J. R., Ahlgren, P. D., Yu, E. et al., “Infrared imaging of the breast: initial reappraisal using high-resolution digital technology in 100 successive cases of stage I and stage II breast cancer,” Breast J. 1998; 4: 245–251. [19] Cunningham, L., “The anatomy of the arteries and veins of the breast,” J Surg Oncol. 1977; 9: 71–85. [20] Wirth, M. A., Narhan, J., Gray, D., “Nonrigid mammogram registration using mutual information,” Proc. SPIE. 2002; 4684. [21] Zitova, B., Flusser, J., “Image registration methods: a survey,” Image Vis. Comput. 2003; 21 (11): 977–1000. [22] Crum, W. R., Hartkens, T., Hill, D. L., “Non-rigid image registration: theory and practice,” Br. J. Radiol. 2004; 77 (2): S140–S153. [23] Fischer, B., Modersitzki, J., “Curvature based registration with applications to MR-mammography,” In Computational Science. 2002, ICCS. [24] Friston, K. J., Ashburner, J., Frith, C. D. et al., “Spatial registration and normalization of images,” Hum. Brain Mapp. 1995; 2: 165–189. [25] Kaneko, S., Satoh, Y., Igarashi, S, “Using selective correlation coefficient for robust image registration,” Pattern Recognition. 2003 May; 36 (5): 1165–1173. [26] Roche, A., Malandain, G., Pennec, X. et al., “The correlation ratio as a new similarity measure for multimodal image registration,” In Medical Image Computing and Computer-Assisted Intervention. Wells, W. M., Colchester, A., Delp, S. Eds. 1998; 1496: 1115–1124. [27] Bruckner, T., Lucht, R., Brix, G., “Comparison of rigid and elastic matching of dynamic magnetic resonance mammographic images by mutual information,” Medical Physics. 2000; 27 (10): 2456–2461. [28] Maes, F., Collignon, A., Vandermeulen, D. et al., “Multimodality image registration by maximization of mutual information,” IEEE Transactions on Medical Imaging. 1997; 16 (2): 187–198. [29] Wells III, W. M., Viola, P., Atsumi, H. et al., “Multi-modal volume registration by maximization of mutual information,” Medical image analysis. 1996; 1, In press. [30] Reichenbach, J. R., Hopfe, J., Bellemann, M. E. et al., “Development and validation of an algorithm for registration of serial 3D MR breast data sets,” Magn Reson Mater Phys Biol Med. 2002; 14: 249–258. [31] Pluim, J. P. W., Maintz, J. B. A., Viergever, M. A., “Image registration by maximization of combined mutual information and gradient information,” IEEE Transactions on Medical Imaging. 2000; 19 (8): 809–814. [32] Rueckert, D., Sonoda, L. I., Hayes, C. et al., “Nonrigid registration using free-form deformations: Application to breast MR images,” IEEE Transactions on Medical Imaging. 1999; 18(8): 712–721. [33] Studholme, C., Hill, D. L. G., Hawkes, D. J., “An overlap invariant entropy measure of 3D medical image alignment.” Pattern Recognition. 1999; 32: 71–86. [34] Hawkes, D. J., Barratt, D., Blackall, J. M. et al., “Tissue deformation and shape models in image-guided interventions: a discussion paper,” Med. Image Anal. 2005; 9: 163–75. [35] Kim, B., Boes, J. L., Frey, K. A. et al., “Mutual information for automated unwarping of rat brain autoradiographs,” NeuroImage. 1997; 5 (1): 31–40. [36] Rohr, K., Stiehl, H. S., Sprengel, R. et al., “Point based elastic registration of medical image data using approximating thin-plate splines,” Proceedings of the Visualization in Biomedical Computing. 1996; 297–306. [37] Davis, M. H., Khotanzad, A., Flamig, D. P. et al. “A physics-based coordinate transformation for 3-D image matching,” IEEE Transactions on Medical Imaging. 1997; 16 (3): 317–328. [38] Y. Bentoutou, N. Taleb, M. Chikr El Mezouar, M. Taleb, J. Jetto, An invariant approach for image registration in digital subtraction angiography, Pattern Recognition 35 (2002) 2853–2865. [39] Kostelec, P. J., Weaver, J. B., Healy Jr., D. M., “Multiresolution elastic image registration,” Med. Phys. 1998; 25: 1593–1604. [40] W. Peckar, C. Schnorr, K. Rohr, H.S. Stiehl, Two step parameter free elastic image registration with prescribed point displacements, Journal of Mathematical Imaging and Vision 10 (1999) 143–162. [41] Shen, D., Davatzikos, C., “Very high-resolution morphometry using mass preserving deformations and HAMMER elastic registration,” Neuroimage. 2003; 18: 28-41. [42] Agostino, E. D., Maes, F., Vandermeulen, D. et al., “A viscous fluid model for multimodal nonrigid image registration using mutual information,” In Proc. Medical Image Computing and Computer-Assisted Intervention (MICCAI’02). 2002 Sept; 2489: 541–548. [43] Bro-Nielsen, M., Gramkow, C., “Fast fluid registration of medical images,” In Proceedings Visualization in Biomedical Computing (VBC’96). 1996; 1131: 267–276. [44] Christensen, G., “Image-based dose planning of intracavitary brachytherapy: Registration of serial-imaging studies using deformable anatomic templates,” Int. J. Radiat. Oncol. Biol. Phys. 2001; 51: 227–243. [45] Lef´ebure, M., Cohen, L. D., “Image registration, optical flow and local rigidity,” Journal of Mathematical Imaging and Vision. 2001 Mar; 14 (2): 131–147. [46] Keeling, S., Ring, W., “Medical image registration and interpolation by optical flow with maximal rigidity,” Journal of Mathematical Imaging and Vision. 2005; 23: 47-65. [47] Guerrero, T., Zhang, G., Huang, T. C. et al. “Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method,” Phys. Med. Biol. 2004; 49: 4147–61. [48] Lester, H., Arridge, S. R., “A survey of hierarchical non-linear medical image registration,” Pattern Recognition. 1999; 32: 129–149. [49] Guo, Y., Sivaramakrishna, R., Lu, C.-C. et al. “Laxminarayan. Breast image registration techniques: a survey,” Medical and Biological Engineering and Computing. 2006 March; 44 (1-2): 15–26. [50] Meyer, C. R., Boes, J. L., Kim, B. et al., “Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations,” Medical Image Analysis. 1997; 1 (3): 195–206. [51] Likar, B., Pernuˇs, F., “Registration of serial transverse sections of muscle fibres,” Cytometry. 1999: 37 (2): 93–106. [52] Likar, B., Pernuˇs, F., “A hierarchical approach to elastic registration based on mutual information,” Image and Vision Computing. 2001; 19 (1-2): 33–44. [53] Schaefer, G., Tait, R., Zhu, S., “Overlay of thermal and visual medical images using skin detection and image registration,” 28th IEEE Int. Conference Engineering in Medicine and Biol¬og. 2006; 965-967. [54] Hamdi, M., Würinger, E., Schlenz, I. et al. “Anatomy of the Breast: A Clinical Application,” In Vertical Scar Mammaplasty. 2005; 1-8., Springer. [55] Lauterbur, P. C., “Image formation by induced local interactions: examples employing nuclear magnetic resonance,” Nature. 1973; 242: 190–191. [56] Kriege, M., Brekelmans, C., Boetes, C. et al., “Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition,” N Engl J Med. 2004; 351: 427-437. [57] Jackson, V. P., “The role of US in breast imaging,” Radiology. 1990; 177: 305-311. [58] Hardy, J. D., “Summary Review of the Influence of Thermal Radiation on Human Skin,” U.S. Naval Air Development Center, Johnsville, Pa., Rept. No. NADC.uVA-5415. [59] Gautherie, M., “Thermopathology of breast cancer: Measurement and analysis of in vivo temperature and blood flow,” Ann NY Acad. Sci. 1980; 335: 383-413. [60] Duchon, J., “Splines Minimizing Rotation-Invariate Semi-Norms in Sobolev Spaces,” In Constructive Theory of Functions of Several Variables, Schempp, W., Zeller, ed., 1979; 85-100, Springer. [61] Bookstein, F. L., “Principal warps: Thin-plate splines and the decomposition of deformations,” IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989; 11: 567–585. [62] Thompsond, W., “On growth and form,” Cambridge University. 1942; 793. [63] Wink, O., Niessen, W. J., Viergever, M. A.,. “Multiscale vessel tracking,” IEEE Transactions on Medical Imaging. 2004; 23 (1): 130–133. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23384 | - |
dc.description.abstract | 根據行政院衛生署統計,乳癌一直都是女性主要的致死病症之一,唯有早期的發現及完善的治療監控才能降低乳癌對於台灣女性的威脅。然而,至今仍然沒有一種檢查方法可有效的達到此目標。所以本實驗室近年來著重於發展一套量化型雙波段紅外線影像分析系統,嘗試追蹤高溫組織隨著時間變化的資訊以量化分析化學治療之效果。本研究之主要目的即為發展其中關鍵技術—多時間之雙波段紅外線影像對位演算法。
本實驗中,影像對位演算法是要將經過雙波段對位後的多時間點紅外線乳房影像形變為同一幾何結構,使相對應之組織移動到相同位置以取得更多有用的資訊。其演算法大約分為三個步驟,首先利用Harris角落點偵測演算法(Harris corner detector)偵測出目標影像(target image)及欲對位之來源影像(source image)中的角落點,以及分析Hessian矩陣找出血管中線之交叉點。接著利用手動選擇的方式建立兩張影像中角落點及血管交叉點的相互關係,以形成一組應用於薄板仿樣法(Thin Plane Spline, TPS)之相對應點(corresponding points)。最後利用Nelder-Mead單體法(simplex method)來修正來源影像中控制點的位置,並使得共同訊息(mutual information, MI)達到最大值,以得到最佳化對位結果。 為了評估多時間點紅外線影像對位演算法的準確性,本研究拍攝了十位正常受測者並模擬不同時間點造成的乳房形狀及熱圖譜的改變。實驗結果顯示,本研究所提出的對位演算法具有一定的精準度,其最大誤差值約為1.57個pixels。我們也將對位演算法應用在化療病患的雙波段紅外線序列影像中,並利用DS-HPS演算法計算出有效高溫組織含量。其結果顯示,乳癌部位之高溫組織含量會隨著時間下降,這也證明了結合多時間點紅外線影像對位演算法的雙波段紅外線系統,對於乳癌的偵測及化療的追蹤評估具有一定的潛力。 | zh_TW |
dc.description.abstract | According to the statistical data from Department of Health, breast cancer has been one of major cancer diseases causing death for a long time. Detecting breast cancer early and monitoring the effect of chemotherapy completely could reduce the death rate of breast cancers. However, there were few methods to achieve the goal effectively. Therefore, recently, our team has been developing the Quantified Dual Spectrum-Infrared (QDS-IR) system to quantify the effect of chemotherapy by adding the information of heat changing with time. The aim of this study was to develop one of the key technologies of the QDS-IR system, i.e., the registration algorithm for longitudinal dual spectrum infrared images.
The proposed image registration algorithm was composed of three steps. First, the corner points on the heat pattern were detected by Harris corner detector algorithm and the intersections of blood vessels were found via the Hessian matrix. Second, the relationships of the corner points and the intersections of blood vessels were established between the source image and the target image manually to get a set of corresponding point for Thin Plane Spline (TPS) model. Third, the Nelder-Mead simplex method was used to modify the locations of control points on the source image via maximizing the mutual information (MI) value. In the mean time, optimal and sub-pixel registration could be achieved. To evaluate the accuracy of the longitudinal infrared image registration algorithm, we took the sequences infrared images of ten normal subjects, and simulated the changes of the breast shape caused by poses and the variation of the heat patterns at different time points. The experiment results showed that the maximal registered error was about 1.57 pixels. We applied the proposed registration algorithm to the breast cancer patients who need to do chemotherapy, and used DS-HPS algorithm to quantify the effective volume of high temperature tissues. The results showed that the effective volume of tumor tissues would decrease with time. The QDS-IR system with longitudinal image registration had potential to assess the effect of chemotherapy and further to detect breast cancers. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T05:00:14Z (GMT). No. of bitstreams: 1 ntu-99-R97548011-1.pdf: 15305325 bytes, checksum: a430a3588f15668f2ffb8a622d70c3ef (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 誌謝......................................I
中文摘要.................................II 英文摘要................................III 目錄.....................................IV 圖目錄...................................VI 表目錄...................................IX 第一章 緒論............................1 1.1 研究背景...........................1 1.2 研究動機...........................3 1.3 文獻回顧...........................4 1.3.1 相似性測量指標.................5 1.3.2 形變模型.......................6 1.3.3 最佳化過程.....................7 1.4 研究目的...........................8 1.5 研究架構...........................9 第二章 基礎理論.......................10 2.1 乳房解剖學........................10 2.2 乳癌分類..........................11 2.3 磁振造影..........................12 2.4 X-ray 乳房攝影....................14 2.5 乳房超音波........................15 2.6 正子斷層掃描......................17 2.7 紅外線熱造影......................18 2.8 紅外線熱造影基礎理論..............19 2.9 雙波段紅外線影像原理..............22 第三章 研究材料與方法.................24 3.1 研究材料..........................24 3.2 紅外線攝影系統....................25 3.3 軟體分析方法......................27 3.3.1 雙波段壞線影像對位............28 3.3.1.1 薄板仿樣法................28 3.3.1.2 雙線性內插法..............31 3.3.2 多時間點紅外線影像對位........31 3.3.2.1 Harris偵測演算法..........33 3.3.2.2 血管骨架搜尋..............34 3.3.2.3 共同訊息..................36 3.3.2.4 Nelder-Mead單體法.........37 3.3.3 雙波段熱圖譜分離演算法........39 第四章 研究成果與討論.................42 4.1 軟體分析結果......................42 4.1.1 雙波段紅外線影像對位結果......42 4.1.2 多時間點紅外線影像對位結果....44 4.1.3 DS-HPS演算法結果..............48 4.2 多時間點影像對位演算法驗證........49 4.3 化學治療效果之量化結果............51 第五章 結論與未來展望.................63 參考文獻.................................65 附錄A 正常受測者對位結果.................71 | |
dc.language.iso | zh-TW | |
dc.title | 乳癌化療病患之多時間點紅外線影像對位 | zh_TW |
dc.title | Image Registration of Longitudinal Infrared Images for Breast Cancer Patients Receiving Chemotherapy | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 許志宇,張允中 | |
dc.subject.keyword | 乳癌,影像對位,Harris角落點偵測演算法,血管中線,薄板仿樣法,Nelder-Mead單體法,共同訊息, | zh_TW |
dc.subject.keyword | Breast cancer,image registration,Harris corner detector,middle lines,Thin Plane Spline,Nelder-Mead simplex method,mutual information, | en |
dc.relation.page | 86 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2010-08-17 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
顯示於系所單位: | 醫學工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-99-1.pdf 目前未授權公開取用 | 14.95 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。