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MRI Based Acoustic Neuroma Image Segmentation System
|關鍵字:||Acoustic neuroma;聽神經瘤;magnetic resonance image (MRI);image segmentation;edge detection;region growing;核磁共振影像;影像切割;邊緣偵測;區域成長||出版社:||資訊管理學系所||引用:|| J. Canny, “A computational approach to edge detection,” 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-698.  Y. K. Chan, and C. C. Chang, “Image matching using run-length feature,” 2001, Pattern Recognition Letters, vol. 22, no. 5, pp. 447-455.  E. Davies, “Machine vision: theory, algorithms and practicalities,” 1990, Academic Press (Chapter 5).  R. C. Gonzalez, and R. E. Woods, “Digital image processing, 3rd edition,” 2008, Prentice Hall.  T. Hain, “Acoustic neuroma,” 2010, (http://www.dizziness-and-balance.com/disorders/tumors/acoustic_neuroma.htm).  D. C. Huang, R. T. Chen, Y. K. Chan, and X. Jiang, “An automatic indirect immunofluorescence based cell segmentation and counting system,” 2010, National Digital Library of Theses and Dissertations in Taiwan.  International RadioSurgery Association, “Acoustic neuroma,” (http://www.irsa.org/acoustic_neuroma.html).  M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” 1987, International Journal of Computer Vision, vol. 1, no.4, pp. 321-331.  A. Komatsuzaki, and A. Tsunoda, 2001, “Nerve origin of the acoustic neuroma,” The Journal of Laryngology and Otology, vol. 115, no. 5, pp. 376-379.  C. M. Li, C. Y. Xu, C. F. Gui, and M. D. Fox, “Level set evolution without re-initialization: a new variational formulation,” 2005, IEEE Computer Society Conference on Computer Version Pattern Recognition.  K. F. Man, K. S. Tang, and S. Kwong, “Genetic algorithms: concepts and designs,” 1999, Springer-Verlag.  J. E. Muscat, M. G. Malkin, R. E. Shore, S. Thompson, A. I. Neugut, S. D. Stellman, and J. Bruce, “Handheld cellular telephones and risk of acoustic neuroma,” 2002, Neurology, vol. 58, no. 8, pp. 1304-1306.  S. Osher, and J. A. Sethian, “Fronts propagating with curvaturedependent speed: algorithms based on Hamilton-Jacobi formulations,” 1988, Journal of Computer Physics, vol. 79, pp. 12-49.  N. Otsu, “A threshold selection method from gray-level histogram,” 1979, IEEE Transactions on System Man Cybernetics, vol. SMC-9, no. 1, pp. 62-66.  Scientific Learning Corporation, “MRI for IAC,” 1999, (www.BranConnection.com).  J. Serra, “Image analysis and mathematical morphology, 1st edition,” 1983, Academic Press.  M. Sezgin, and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” 2004, Journal of Electronic Imaging, vol. 13, no. 1, pp. 146-165.  Taipei Municipal WanFang Medical Center, “Acoustic Neuroma,” (www.wanfang.gov.tw/ckc/).  U.S. National Library of Medicine, and National Institute of Health, “Acoustic neuroma,” (http://www.nlm.nih.gov/medlineplus/acousticneuroma.html).  Wikipedia the free encyclopedia, “Level Set Method,” (http://en.wikipedia.org/wiki/Level_set_method).  Wikipedia the free encyclopedia, “Vestibular Schwannoma,” (http://en.wikipedia.org/wiki/Vestibular_schwannoma).  C. J. Xu, and L. Prince, “Snakes, shapes, and gradient vector flow,” 1998, IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 359-369.  S. F. Yang-Mao, Y. K. Chan, and Y. P. Chu, “Edge enhancement nucleus and cytoplast contour detector of cervical smear images,” 2008, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 38, no. 2, pp. 353-366.||摘要:||
本研究針對聽神經瘤之核磁共振影像進行腫瘤之偵測與切割。聽神經瘤為顱內腫瘤之一，通常生長於中小腦與橋腦所形成之夾角。聽神經瘤是由於前庭神經許旺細胞過度增生之結果。一般來說，聽神經瘤為良性腫瘤，但仍有機會成為惡性腫瘤，進而擴散至身體其他部位影響正常器官運作甚至造成死亡。在此論文中，利用醫師所拍攝之聽神經瘤核磁共振影像，結合邊緣偵測與區域成長之數位影像切割技術，以完成每位病患整組聽神經瘤核磁共振影像之腫瘤位置偵測。運用系統使用者介入所點選的像素點作為聽神經瘤影像切割之種子點，進而進行邊緣梯度偵測、邊緣強化以及去雜訊等方式描繪出腫瘤物件之邊緣，並結合腫瘤顏色特性之區域成長，切割出完整之聽神經瘤。本研究之目的在於解決傳統醫學上腫瘤偵測與定位需耗費大量時間與專業人力成本之缺點，並能夠提升聽神經瘤治療與診斷之效率。本論文利用ACM與LSM兩種常用之影像切割方法進行最終實驗結果比對，並利用四種常用的物件切割指標(ME, RAE, MHD, RDE)量化實驗結果。實驗結果顯示，本研究方法在大部分的聽神經瘤之核磁共振影像切割準確度方面優於其他兩種方法。
This research proposes a MRI based acoustic neuroma image segmentation system. Acoustic neuroma, also known as vestibular schwannoma, is one kind of intracranial tumor. Most commonly, acoustic neuroma arises in a wedge shaped area bounded by the petrous bone, the pons and the cerebellum. Acoustic neuroma results from abnormal hyperplasia of Schwann cells of the inferior vestibular nerve. Generally, acoustic neuroma is often a benign tumor; however, it still has potential probability to become a malignant tumor that usually grows faster and tends to spread to other organs which may seriously harm human bodies or even cause death. Acoustic neuroma MR images which are scanned by the doctor are used in this research to detect the location of tumors. The involvement of the user is needed in this research. A seed point is assigned by the user, and edge based segmentation methods such as gradient calculation, edge enhancement, and noise reduction are used to illustrate edge of the acoustic neuroma in MRI. Besides, a region growing method is used to get the intensity feature of the acoustic neuroma. The combination of edge based segmentation methods and the region growing method provides a good result in acoustic neuroma segmentation. The goal of this research is to solve great amount of waste in medical resource and time consuming in traditional acoustic neuroma detection. ACM and LSM are used to investigate the performance of the proposed method. In order to quantitate segmentation results, four commonly used segmentation error measures (ME, RAE, MHD, and RDE) are used in this research. The results show that the proposed method is better than other two methods with regards to segmentation accuracy.
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