Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19486
標題: 醫學影像中物件邊緣偵測之研究
A Study on Object Contour Detection in the Medical Images
作者: 楊茆世芳
Shys-Fan, Yang-Mao
關鍵字: Medical Image Processing;醫學影像處理;Image Segmentation;Retinal Fundus Images;Pap Smear Images;影像切割;視網膜眼底影像;子宮頸抹片影像
出版社: 資訊科學與工程學系所
引用: [1] Bamford, P. and Lovell, B.: A Methodology for Quality Control in Cell Nucleus Segmentation, Digital Image Computing: Techniques and Applications, 21-25, 1999. [2] Busam, K. J., Hester, K., Charles, C., Sachs, D. L., Antonescu, C. R., Gonzalez, S. and Halpern, A.: Detection of Clinically Amelanotic Malignant Melanoma and Assessment of its Margins by in Vivo Confocal Scanning Laser Microscopy, Archives of Dermatology, 137(7):923-929, 2001. [3] Canny, J.: A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698, 1986. [4] Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M. and Goldbaum, M.: Detection of Blood Vessels in Retinal Images Using Two-dimensional Matched Filters, IEEE Transactions on Medical Imaging, 8:263-269, 1989. [5] Collier, T., Lacy, A., Malpica, A., Follen, M. and Richards-Kortum, R.: Near Real-Time Confocal Microscopy of Amelanotic Tissue: Detection of Dysplasia in Ex-Vivo Cervical Tissue, Engineering in Medicine and Biology, Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, 2:979-981, 2002. [6] Cootes, T. F., Taylor, C. J., Cooper, D. H. and Graham, J.: Active Shape Model - Their Training and Application, Computing Vision and Image Understanding, 61(1):38-59, 1995. [7] Corcuff, P., Gonnord, G., Pierard, G. E. and Leveque, J. L.: In Vivo Confocal Microscopy of Human Skin: A New Design from Cosmetology and Dermatology, Scanning, 18(5):351-355, 1996. [8] Dadeshidze, V., Olsson, L. and Domanik, R. A.: Segmentation of Nuclear Images in Automated Cervical Cancer Screening, SPIE Society for Optical Engineering, 2622:723-727, 1995. [9] Davies, E.: Machine Vision: Theory, Algorithms and Practicalities, Academic Press, 1990. [10] Foracchia, M., Grisan, E. and Rugger, A.: Detection of Optic Disc in Retinal Images by Means of a Geometrical Model of Vessel Structure, IEEE Transactions on Medical Imaging, 23(10):1189-1195, 2004. [11] Frable, W. J.: Needle Aspiration Biopsy of Pulmonary Tumors, Seminars in Respiratory Medicine, 4(2):161-169, 1982. [12] Gonzalez, R. and Woods, R.: Digital Image Processing, Prentice-Hall, 2002. [13] Hoover, A. and Goldbaum, M.: Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter Response, IEEE Transactions on Medical Imaging, 19(3):203-210, 2000. [14] Hoover, A. and Goldbaum, M.: Locating the Optic Nerve in Retinal Image Using the Fuzzy Convergence of the Blood Vessels, IEEE Transactions on Medical Imaging, 22(8):951-958, 2003. [15] Hou, Z., Hu, Q. and Nowinski, W. L.: On Minimum Variance Thresholding, Pattern Recognition Letters 27:1732-1743, 2006. [16] Inoue, H., Igari, I., Nishikage, T., Ami, K., Yoshida, T. and Iwai, T.: A Novel Method of Virtual Histopathology Using Laser Scanning Confocal Microscopy in Vitro with Untreated Fresh Specimens from the Gastrointestinal Mucosa, Endoscopy, 32(6):439-443, 2000. [17] Langley, R. G. B., Rajadhyaksha, M., Dwyer, P. J., Sober, A. J., Flotte, T. J. and Anderson, R. R.: Confocal Scanning Laser Microcopy of Benign and Malignancy Melanocytic Skin Lesions in Vivo, Journal of the American Academy of Dermatology, 45(3):365-376, 2001. [18] Li, H. and Chutatape, O.: Automatic Location of Optic Disk in Retinal Images, Proceedings of IEEE International Conference on Image Processing, 837-840, 2001. [19] Li, H. and Chutatape, O.: Boundary Detection of Optic Disc by a Modified ASM Method, Pattern Recognition, 36(9):2093-2104, 2003. [20] Li, Y., Cheriet, M. and Suen, C. Y. : A Threshold Selection Method based on Multiscale and Graylevel Co-occurrence Matrix Analysis, Proceedings of the International Conference on Document Analysis and Recognition, 2005. [21] Luck, B. L., Carlson, K. D., Bovik, A. C. and Richards-Kortum, R. R.: An Image Model and Segmentation Algorithm for Reflectance Confocal Images of in Vivo Cervical Tissue, IEEE Transactions on Image Processing, 14(9):1265-1276, 2005. [22] Martin, E.: Pap-smear Classification, Master's thesis, Technical University of Denmark: Oersted-DTU, Automation, 2003. [23] Masters, B. R., Aziz, D. J., Gmitro, A. F., Kerr, J. H., O'Grady, T. C. and Goldman, L.: Rapid Observation of Unfixed Unstained Human Skin Biopsy Specimens with Confocal Microscopy and Visualization, Journal of Biomedical Optics, 2(4):437-445, 1997. [24] Mat-Isa, N. A., Mashor, M. Y. and Othman, N. H.: Seeded Region Growing Features Extraction Algorithm; its Potential Use in Improving Screening for Cervical Cancer, International Journal of the Computer, the Internet and Management, 13(1):61-70, 2005. [25] Morris, D. T. and Donnison, C.: Identifying the Neuroretinal Rim Boundary Using Dynamic Contour, Image and Vision Computing, 17(3/4):169-174, 1999. [26] National Cancer Institute: NCI Women''s Health Report Fiscal Years 2005-2006, http://women.cancer.gov/planning/. [27] Ng, H. F.: Automatic Thresholding for Defect Detection, Pattern Recognition Letters 27:1644-1649, 2006. [28] Norup, J.: Classification of Pap-smear Data by Transductive Neuro-Fuzzy Methods, Master's thesis, Technical University of Denmark: Oersted-DTU, Automation, 2005. [29] Osareh, A., Mirmehdi, M., Thomas, B. and Markham, R.: Color Morphology and Snakes for Optic Disc Location, Proceeding of the 6th Medical Image Understanding and Analysis Conference, BMVA Press, 21-24, 2002. [30] Otsu, N.: A Threshold Selection Method from Gray-level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9(1):62-66, 1979. [31] Rajadhyaksha, M., Grossman, M., Esterowitz, D., Webb, R. H. and Anderson, R. R.: In Vivo Confocal Scanning Laser Microscopy of Human Skin: Melanin Provides Strong Contrast, Journal of Investigative Dermatology, 104(6):946-952, 1995. [32] Rajadhyaksha, M., Gonzalez, S., Zavislan, J. M., Anderson, R. R. and Webb, R. H.: In Vivo Confocal Scanning Laser Microscopy of Human Skin II: Advances in Instrumentation and Comparison with Histology, Journal of Investigative Dermatology, 113:293-303, 1999. [33] Ridler, T. W. and Calvard, S.: Picture Thresholding Using an Iterative Selection Method, IEEE Transactions System, Man and Cybernetics, 8:630-632, 1978. [34] Russo, F. and Lazzari, A.: Color Edge Detection in Presence of Gaussian Noise Using Nonlinear Prefiltering, IEEE Transactions on Instrumentation and Measurement, 54(1):352-358, 2005. [35] Sezgin, M. and Sankur, B.: Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation, Journal of Electronic Imaging, 13(1):146-165, 2004. [36] Sinthanayothin, C., Boyce, J. F., Cook, H. L. and Williamson, T. H.: Automated Location of the Optic Disc, Fovea, and Retinal Blood Vessels from Digital Color Fundus Images, British Journal of Ophthalmology, 38(8):902-910, 1999. [37] Su, M. C. and Chou, C. H.: A Modified Version of K-means Algorithm with a Distance Based on Cluster Symmetry, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):674-680, 2001. [38] Turk, M. and Pentland, A. P.: Eigenfaces for Recognition, Journal of Cognitive Neuroscience, 3(1):71-86, 1991. [39] Walker, R. F.: Adaptive Multi-scale Texture Analysis with Application to Automated Cytology, PhD thesis, Department of Electrical and Computer Engineering, University of Queensland, Dissertation, 1997. [40] Wu, H. S., Gil, J. and Barba, J.: Optimal Segmentation of Cell Images, IEE Proceeding Visual, Image and Signal Processing, 145(1):50-56, 1998. [41] Xu, C. Y. and Prince, J. L. : Snakes, Shapes, and Gradient Vector Flow, IEEE Transactions on Image Processing, 7(3):359-369, 1998. [42] Yin, L., Basu, A. and Chang, J. K.: Scalable Edge Enhancement with Automatic Optimization for Digital Radiographic Images, Pattern Recognition, 37(7):1407-1422, 2004. [43] Zhang, J., Yan, Y. and Lades, M.: Face Recognition: Eigenface, Elasticity Matching, and Neural Nets, Proceedings of the IEEE, 85(9):1423-1435, 1997.
摘要: 
醫學造影係指用醫學儀器對人體或是部分組織做造影,並且利用這些影像做醫學應用。現有很多這樣的造影技術:光學影像、MRI、CT、PET或是超音波等等。醫生可以分析這些影像上物件的位置和特徵來做診斷。可是,當需要分析的影像數量很大時,光是靠人工來一張一張診斷,那會相當沒有效率。為了加快分析影像的速度,我們可以利用電腦科學來自動分析這些影像。
醫學影像分析大致上可以分成三個步驟。第一個是影像前處理,包含去雜訊和加強對比等等影像加強的過程。第二個步驟是切割出影像中有興趣的區塊(ROI),也就是影像中的物件。第三步驟就是將切割出來的物件做更進一步的醫學分析。其中最重要的就是前兩個步驟,影像加強結果會直接影響到切割的正確性,而切割的方法也要針對影像的不同來做調整。
在這論文裡我們針對視網膜影像和子宮頸影像來做研究。視網膜影像中要切割出光盤的位置,而子宮頸抹片影像是要切割出細胞膜和細胞質的位置。這兩種影像都很容易受到雜訊和影像品質的影響而讓切割結果不佳。為了解決這些問題,我們在這論文裡提出了幾個自動影像分析的技術。實驗結果顯示這裡提出的技術的效果讓人印象深刻。除了視網膜影像和子宮頸抹片影像之外,這些技術也可以應用在其他影像的物件邊緣偵測。

Medical imaging designates the ensemble of techniques and processes used to create images of the human body or parts tissue for clinical purposes. There are several techniques to make those images, including the microscopy imaging, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET) or ultrasonography. The doctor can read these pictures to make diagnosis by analyze the locations or texture features of the objects in those medical images. However, if there are too many images that need to be analyzed, it is time consuming that all by manual. To speed up the time of analysis, it requires the computer science to automatic analysis those medical images.
There are three main procedures in medical image analysis. The first procedure is image preprocessing, which is the image enhancement that includes image denoise and contrast enhancement. The second procedure is the region of interest (ROI) segmentation, also called object segmentation. The third procedure is the further object analysis, which is the medical knowledge based image analysis. The most important procedures are the first and second ones, the result of image enhancement will affect the accuracy of object segmentation; and the object segmentation required several tuning for different images.
In this thesis, we focus on retinal fundus and cervical smear images. The main goal is locating the optical disc contours from fundus images and the nucleus or cytoplast contours from Pap-smear images. The accuracy of objects segmentation on those images is very sensitive to noise and poor image quality. To solve this question, we propose several automatic image segmentation techniques in this thesis. The experimental results show that all the techniques proposed here have performed impressively. Besides cervical smear images or retinal fundus images, these techniques can also be utilized in detecting object contours on other images.
URI: http://hdl.handle.net/11455/19486
其他識別: U0005-2712200721210200
Appears in Collections:資訊科學與工程學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.