請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22633完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 翁朝旻(Jau-Min Wong) | |
| dc.contributor.author | Chun-Chih Liao | en |
| dc.contributor.author | 廖俊智 | zh_TW |
| dc.date.accessioned | 2021-06-08T04:22:57Z | - |
| dc.date.copyright | 2010-07-02 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-06-29 | |
| dc.identifier.citation | REFERENCES
[1] Dorland WAN: Dorland's illustrated medical dictionary, 28th Ed. W.B. Saunders Company, 1994. [2] M.R. Bullock, R. Chesnut, J. Ghajar, D. Gordon, R. Hartl, D.W. Newell, F. Servadei, B.C. Walters, J.E. Wilberger, Evaluation of relevant computed tomographic scan findings, Neurosurgery 58 (3 Suppl 2) (2006) 62. [3] Bullock, M.R., Chesnut, R., Ghajar, J., Gordon, D., Hartl, R., Newell, D.W., Servadei, F., Walters, B.C., Wilberger, J.E.: Surgical management of acute epidural hematomas. Neurosurgery 58, S7--15 (2006) [4] Bullock, M.R., Chesnut, R., Ghajar, J., Gordon, D., Hartl, R., Newell, D.W., Servadei, F., Walters, B.C., Wilberger, J.E.: Surgical management of acute subdural hematomas. Neurosurgery 58, S16--24 (2006) [5] Bullock, M.R., Chesnut, R., Ghajar, J., Gordon, D., Hartl, R., Newell, D.W., Servadei, F., Walters, B.C., Wilberger, J.E.: Surgical management of traumatic parenchymal lesions. Neurosurgery 58, S25--46 (2006) [6] Broderick, J.P., Adams, H.P., Barsan, W., Feinberg, W., Feldmann, E., Grotta, J., Kase, C., Krieger, D., Mayberg, M., Tilley, B., Zabramski, J.M., Zuccarello, M: Guidelines for the management of spontaneous intracerebral hemorrhage. Stroke 30, 905--915 (1999) [7] Broderick, J., Connolly, S., Feldmann, E., Hanley, D., Kase, C., Krieger, D., Mayberg, M., Morgenstern, L., Ogilvy, C.S., Vespa, P., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults: 2007 update. Stroke 38, 2001--2023 (2007) [8] Kluge, W., Kretzschmar, K., Roesler, A., Grumme, T.H.: Cerebral and Spinal Computed Tomography, 3rd edition. Blackwell Publishers, Berlin (1998) [9] Doi, K: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized Medical Imaging and Graphics 31 (2007) 198–211 [10] Milliken JA, Wartak J, Orme W, Fay JE, Lywood DW, Abrahams VC: Computer recognition of electrocardiographic waveforms. C.M.A. JOURNAL 103 (1970) 365-370 [11] Buchanan BG, Shortliffe EH: Rule-based expert systems. Addison-Wesley, 1984. [12] Koss LG, Lin E, Schreiber K, Elgert P, Mango L: Evaluation of the PAPNET cytologic screening system for quality control of cervical smears. Am J Clin Pathol. 101:220-229, 1994. [13] Liao, C.C., Xiao, F., Wong, J.M., Chiang, I.J.: A knowledge discovery approach to diagnosing intracranial hematomas on brain CT: recognition, measurement and classification. In: International Conference on Medical Biometrics 2008, LNCS 4901, 73-82. Springer-Verlag Berlin, 2007. [14] Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA (1993). [15] Liao CC, Xiao F, Wong JM, Chiang IJ: Automatic diagnosis of intracranial hematoma on brain CT using knowledge discovery techniques: is finer resolution better? Biomed. Eng: Appl. Basis Comm. 20: 401-408, 2008. [16] Liao CC, Xiao F, Wong JM, Chiang IJ: A multiresolution binary level set method and its application to intracranial hematoma segmentation. Comput Med Img Graph, Volume 33, Issue 6, Pages 423-430, 2009. [17] Liao CC, Xiao F, Wong JM, Chiang IJ: Tracing the deformed midline on brain CT. Biomed Eng Appl Basis Comm 18: 305-311, 2006. [18] Liao, C.C., Xiao, F., Wong, J.M., Chiang, I.J.: A simple genetic algorithm for tracing the deformed midline on a single slice of brain CT using quadratic Bézier curves. In: 6th IEEE International Conference on Data Mining Workshops, pp. 463--467. IEEE Press, New York (2006) [19] Liao, C.C., Xiao, F., Wong, J.M., Chiang, I.J.: Automatic recognition of midline shift on brain CT images. Computers in Biology and Medicine, in press. [20] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programming, 3rd ed. Springer-Verlag, New York, NY, 1996. [21] Cheng E, Cheng K, A PC-based medical image analysis system for brain CT hemorrhage area extraction. In: Proc 11th IEEE Symp Comput-Based Med Sys pp 240-245, 1998. [22] Loncaric S, Dhawan AP, Kovacevic D, Cosic D, Broderick J, Brott T: Quantitative intracerebral brain hemorrhage Analysis. In: Proceedings of SPIE Medical Imaging, Vol 3661, pp 886-894, 1999. [23] Pérez N, Valdés JA, Guevara MA, Rodríguez LA, Molina JM: Set of methods for spontaneous ICH segmentation and tracking from CT head images. In: Proc. 12th Iberoamericann Congress on Pattern Recognition (CIARP) 2007. Lecture note on computer science 4756, pp 212–220, 2007. [24] Chan T: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput Med Img Graph 31: 285-298, 2007. [25] Maldjian JA, Chalela J, Kasner SE, Liebeskind D, Detre JA: Automated CT segmentation and analysis for acute middle cerebral artery stroke. Am J Neuroradiol, 22:1050-1055, 2001. [26] Kruggel F, Segmentation of focal brain lesions. In: Proc Med Imaging Augmented Reality: 2nd Intl. Workshop, MIAR2004, Beijing, China, Lecture note on computer science 3150, pp 10–18, 2004. [27] Lee JD, Chang TC, Huang CH, Wu SJ, Chen CJ, Computer-aided diagnosis system for acute stroke using diffusion weighted images with volume calculation. In: Proc 17th Ann. Intl. Conf. IEEE EMBS, pp.1529-1532, 2004. [28] Halberstadt W, Douglas TS: Fuzzy clustering to detect tuberculous meningitis-associated hyperdensity in CT images. Computers in Biology and Medicine 38 (2008) 165 – 170. [29] Hu, Q., Qian, G., Aziz, A., Nowinski, W.L.: Segmentation of brain from computed tomography head images. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 3375--3378. IEEE Press, New York (2005) [30] Cosic, D., Loncaric, S.: A Rule-based Labeling of CT Head Image. Proceedings of the 6th Conference on Artificial Intelligence in Medicine Europe, Grenoble, France, LNAI, pp. 453--456, Springer, Heidelberg (1997) [31] Bullock, M.R., Chesnut, R., Ghajar, J., Gordon, D., Hartl, R., Newell, D.W., Servadei, F., Walters, B.C., Wilberger, J.E.: Post-traumatic mass volume measurement in traumatic brain injury patients. Neurosurgery 58, S61 (2006) [32] Xia, Y., Hu, Q., Aziz, A., Nowinski, W.L.: A knowledge-driven algorithm for a rapid and automatic extraction of the human cerebral ventricular system from MR neuroimages. Neuroimage 21, 269--282 (2004) [33] R. C. Gonzalez, R. E. Woods, Digital Image Processing second edition, Prentice Hall, 2002 [34] Osher S, Fedkiw R: Level set methods and dynamic implicit surfaces. Springer-Verlag, 2003. [35] Song B, Chan TF: A fast algorithm for level set based optimization. UCLA CAM Report 02-68, 2002. [36] Chan TF, Esedoglu S: A multiscale algorithm for Mumford-Shah image segmentation. UCLA CAM Report 03-77, 2003. [37] Berge JK, Bregman RA, Variations in size and in symmetry of foramina of the human skull. Clin Anatomy 14:406-413, 2001. [38] Worth AJ, Makris N, Patti MR, Goodman JM, Hoge EA, Caviness VS, Kennedy DN, Precise Segmentation of the lateral ventricles and caudate nucleus in MR brain images using anatomically driven histograms. IEEE Trans Med Img 17:303-310, 1998. [39] L.F. Marshall, B.M. Toole, S.A. Bowers, The National Traumatic Coma Data Bank. Part 2: Patients who talk and deteriorate: implications for treatment, J. Neurosurg. 59 (2) (1983) 285-288. [40] A.H. Ropper, Lateral displacement of the brain and level of consciousness in patients with an acute hemispheral mass. New Engl. J. Med. 314 (15) (1986) 953-958. [41] A.H. Ropper, A preliminary MRI study of the geometry of brain displacement and level of consciousness with acute intracranial masses. Neurology 39 (5) (1989) 622-627. [42] A.I. Maas, M. Dearden, G.M. Teasdale, R. Braakman, F. Cohadon, F. Iannotti, A. Karimi, F. Lapierre, G. Murray, J. Ohman, L. Persson, F. Servadei, N. Stocchetti, A. Unterberg, EBIC-guidelines for management of severe head injury in adults. European Brain Injury Consortium. Acta Neurochirurgica (Wien) 139 (4) (1997) 286-294. [43] M.R. Bullock, R. Chesnut, J. Ghajar, D. Gordon, R. Hartl, D.W. Newell, F. Servadei, B.C. Walters, J.E. Wilberger, Early indicators of prognosis in severe traumatic brain injury: Computed tomography scan features. J. Neurotrauma 17 (6-7), (2000) 597-627. [44] H. Gray, C.D. Clemente, Gray's Anatomy of the Human Body, 30th ed. Lippincott Williams & Wilkins, Philadelphia, PA, 1984. [45] T. Nagashima, N. Tamaki, S. Matsumoto, B. Horwitz, Y. Seguchi, Biomechanics of hydrocephalus: a new theoretical model, Neurosurgery 21 (6) (1987) 898-904. [46] A. Peña, M.D. Bolton, H. Whitehouse, J.D. Pickard, Effects of brain ventricular shape on periventricular biomechanics: a finite-element analysis. Neurosurgery 45 (1) (1999) 107-118. [47] M.E. Brummer, Hough transform detection of the longitudinal fissure in tomographic head images. IEEE Transac. Med. Imag. 10 (1) (1991) 74-81. [48] B.A. Ardekani, J. Kershaw, M. Braun, I. Kanno, Automatic detection of the mid-sagittal plane in 3-D brain images, IEEE Transac. Med. Imag. 16 (6) (1997) 947-952. [49] Y. Liu, R.T. Collins, W.E. Rothfus, Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images, IEEE Transac. Med. Imag. 20 (3) (2001) 175-192. [50] S. Prima, S. Ourselin, N. Ayache, Computation of the mid-sagittal plane in 3-D brain images, IEEE Transac. Med. Imag. 21 (2) (2002) 122-138. [51] Q. Hu, W.L. Nowinski, A rapid algorithm for robust and automatic extraction of the midsagittal plane of the human cerebrum from neuroimages based on local symmetry and outlier removal. Neuroimage 20(4) (2003) 2153-2165. [52] M.I. Miga, D.W. Roberts, F.E. Kennedy, L.A. Platenik, A. Hartov, K.E. Lunn, K.D. Paulsen, Modeling of retraction and resection for intraoperative updating of images. Neurosurgery 49 (1) (2001) 75-85. [53] A. Watt, M. Watt, Advanced animation and rendering techniques. Theory and practice. ACM Press, New York, NY, 1992. [54] Curé J.L., P. Van Tassel, M.T. Smith, Normal anatomy of the dural venous sinuses. Seminars in Ultrasound, CT, and MRI 15 (6) (1994) 499-519. [55] M.S. Greenberg, Handbook of Neurosurgery, 4th ed. Greenberg Graphics, Lakeland, FL, 1997. [56] N. Relkin, A. Marmarou, P. Klinge, M. Bergsneider, P. M. Black, Diagnosing Idiopathic Normal-pressure Hydrocephalus. Neurosurgery 57 (3 Suppl 2) (2005) 4-16. [57] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programming, 3rd ed. Springer-Verlag, New York, NY, 1996. [58] M. Yamada, H. Watarai, T. Andou, N. Sakai, Fmergency Image Transfer System Through a Mobile Telephone in Japan: Technical Note. Neurosurgery 52 ( 2003) 986-990. [59] G.R. Hillman, C.W. Chang, H. Ying, J. Yen, L. Ketonen, T.A. Kent, A Fuzzy Logic Approach to Identifying Brain Structures in MRI Using Fxpert Anatomic Knowledge. Computers and Biomedical Research, 32 (6) (1999) 503-516. [60] Ross DA, Olsen WL, Ross AM, Andrews BT, Pitts LH, Brain shift, level of consciousness, and restoration of consciousness in patients with acute intracranial hematoma, J Neurosurg 71(4):498-502, 1989 Oct. [61] Liang L, Rehm K, Woods RP Rottenberg DA: Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes using the graph cuts algorithm. NeuroImage 34: 1160-70, 2007. [62] Yuh EL,Gean AD, Manley GT, Callen AL, Wintermark M: Computer-Aided Assessment of Head Computed Tomography (CT) Studies in Patients with Suspected Traumatic Brain Injury. J Neurotrauma 25:1163–1172, 2008. [63] Xia Y, Hu Q, Aziz A, Nowinski WL: A knowledge-driven algorithm for a rapid and automatic extraction of the human cerebral ventricular system from MR neuroimages. NeuroImage 21: 269–82, 2004. [64] Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imag, 13: 716-24, 1994. [65] Hayashi N, Sanada S, Suzuki M, Matsuura Y: Study of automated segmentation of the cerebellum and brainstem on brain MR images. Japanese Journal of Radiological Technology. 2005 Apr 20;61(4):499-505. [66] Lo RC, Tsai WH: Gray-scale Hough transform for thick line detection in gray-scale images. Pattern Recognition, 28: 647-61, 1995 [67] Munkres, James; Topology, Prentice Hall; 2nd edition, 1999. [68] Filipovic B, Teofilovski-Parapid G: Linear parameters of normal and abnormal cava septi pellucidi: a post-mortem study. Clinical Anatomy 17:626–630 (2004) [69] Fennema-Notestine C, Ozyurt B, Clark CP, Morris S, Bischoff-Grethe A, Bondi MW, Jernigan TL, Fischl B, Segonne F, Shattuck DW, Leahy RM, Rex DE, Toga AW, Toga KH, Brown GG: Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location. Hum Brain Mapp 27: 99–113, 2006. [70] Toutant SM, Klauber MR, Marshall LF, Toole BM, Bowers SA, Seelig JM, Varnell JB:Absent or compressed basal cisterns on first CT scan: ominous predictors of outcome in severe head injury. J Neurosurg. 1984 Oct;61(4):691-4. [71] Eisenberg HM, Gary HE Jr, Aldrich EF, Saydjari C, Turner B, Foulkes MA, Jane JA, Marmarou A, Marshall LF, Young HF: Initial CT findings in 753 patients with severe head injury. A report from the NIH Traumatic Coma Data Bank. J Neurosurg. 1990 Nov;73(5):688-98. [72] Marshall LF, Marshall SB, Klauber MR, Van Berkum Clark M, Eisenberg H, Jane JA, Luerssen TG, Marmarou A, Foulkes MA. The diagnosis of head injury requires a classification based on computed axial tomography. J Neurotrauma. 1992 Mar;9 Suppl 1:S287-92. [73] Liu HM, Tu YK, Su CT: Changes of Brainstem and Perimesencephalic Cistern: Dynamic Predictor of Outcome in Severe Head Injury. J Trauma, Volume 38(3), March 1995, pp 330-333 [74] Lu W, Tan J, Floyd R: Automated fetal head detection and measurement in ultrasound images by iterative randomized Hough transform. Ultrasound Med Biol. 2005 Jul;31(7):929-36. [75] Chrastek R, Wolf M, Donath K, Niemann H, Paulus D, Hothorn T, Lausen B, Lammer R, Mardin CY, Michelson G: Automated segmentation of the optic nerve head for diagnosis of glaucoma. Medical Image Analysis 9: 297-314, 2005. [76] Norman GR, Striner DL: Biostatistics: the bare essentials. Mosby, 1994. [77] Qian W, Hou Z, Hu Q, Aamer A, Nowinski WL: Segmentation of the Sylvian fissure in brain MR images, in Proceeding of the Fourth IEEE Symposium on Bioinformatics and Bioengineering, 2004. [78] Sinha U, Kangarloo H: Principal component analysis for content-based image retrieval. RadioGraphics 22:1271–1289, 2002 [79] Gong T, Tan CL, Leong TY, Lee CK, Pang BC, Lim CCT, Tian Q, Tang S, Zhang Z: Text mining in radiology reports. Data Mining, IEEE International Conference on, pp. 815-820, 2008 Eighth IEEE International Conference on Data Mining, 2008. [80] Hripcsak G, Austin JHM, Alderson PO, Friedman C: Use of Natural Language Processing to Translate Clinical Information from a Database of 889,921 Chest Radiographic Reports. Radiology 224:157–163, 2002. [81] Tubbs RS, Salter G, Elton S, Grabb PA, Oakes WJ: Sagittal suture as an external landmark for the superior sagittal sinus. J Neurosurg 94:985–987, 2001. [82] Bisaria KK: Anatomic variations of venous sinuses in the region of the torcular Herophili. J Neurosurg 62:90-95, 1985 [83] Tubbs RS, Salter G, Oakes WJ: Superficial surgical landmarks for the transverse sinus and torcular herophili. J Neurosurg 93:279–281, 2000. [84] Park HK, Bae HG, Choi SK, Chang JC, Cho SJ, Byun BJ, Sim KB: Morphological study of sinus flow in the confluence of sinuses. Clinical Anatomy 21:294–300, 2008. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22633 | - |
| dc.description.abstract | 外傷或中風引起的顱內血腫，會造成腦部的壓迫，是神經學的急症，也是公共衛生的重大議題。即時的診斷與迅速的治療，是改善顱內血腫病人預後的關鍵因素；然而對非專科醫師而言，在電腦斷層影像中評估這些病灶並不容易。本論文探討顱內血腫在電腦斷層影像中的電腦輔助診斷，並結合二值性等位集合法與多解析度處理，提出新的影像分割方法。
我們運用C4.5演算法發展出判斷顱內血腫類型的決策樹，並發現不同解析度的決策樹都具有良好的效能。利用多解析度二值性等位集合法，我們可以在電腦斷層影像中穩定的分割出顱內血腫的區域。在區分腦實質與顱內血腫之前，我們整合解剖學的知識將顱內區域、顱骨及顱外區域分割出來。本系統對顱內血腫的定性及定量診斷，與人類專家的結果相比，有相當高的一致性。本論文的後半段探討顱內血腫造成腦部壓迫的徵象的電腦輔助診斷。我們利用兩種方法來測量中線偏移：運用對稱的方法與運用解剖構造的方法。前者利用二次貝茲曲線與一維對稱的性質找出偏移的中線，並利用基因演算法來進行參數的最佳化；後者則在運用影像分割找出腦室的前角之後，再運用霍夫(Hough)轉換將其中的透明膈辨識出來。最後，我們描述了運用霍夫轉換找出顱底的腦池區域，並判斷此區域被壓迫的程度。 | zh_TW |
| dc.description.abstract | Intracranial hematomas, either traumatic or spontaneous, can produce fatal outcomes because they can produce local pressure on the brain. Accurate diagnosis and rapid decision making are the key factors to good patient outcome. This thesis introduces new methods capable of obtaining the features of the intracranial hematomas in brain CT images. In addition, a new approach of image segmentation integrating binary level set method and multi-resolution processing is proposed.
We develop the decision rules to recognize the type of the intracranial hematoma on CT slices with large intracranial hematomas using C4.5 algorithm. These decision rules work well in different resolutions. To obtain robust segmentation of the intracranial hematoma regions, we introduce a multi-resolution binary level set method using image pyramids and apply it to hematoma segmentation. Prior to segmentation of the hematoma from the brain, anatomical knowledge is integrated with image processing techniques in the segmentation of intracranial regions. The results show excellent precision and recall as verified by human experts. In the second half of this thesis, we describe two methods for automatic measurement of the midline shift (MLS). The first one employs symmetry and curve fitting to measure the MLS of the CT slice at the level of Foramen of Monro. Genetic algorithm is used for parameter optimization. Landmark-based MLS recognition is carried out by first segmenting the frontal horn region followed by a knowledge-driven rule. Hough transform (HT) is then applied to locate the septum pellucidum. Finally, we describe automatic recognition of the basal cisterns using HT. This method is able to pick out the normal or compressed basal cistern region from the given CT data set. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T04:22:57Z (GMT). No. of bitstreams: 1 ntu-99-D95548001-1.pdf: 5642008 bytes, checksum: 17c04e1781d505f9cf1ae23bd000839b (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | CONTENTS
口試委員會審定書 # 序： midnight shift, midline shift i 中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES x LIST OF TABLES xv Chapter 1 Introduction 1 1.1 Clinical significance 1 1.2 Diagnosis of intracranial hematomas on brain CT images 1 1.3 Computer-aided Diagnosis 5 1.4 Thesis goals 6 Chapter 2 Computer-Aided Diagnosis (CAD) of Brain Images 9 2.1 CAD for traumatic intracranial hemorrhage 10 2.2 CAD for stroke 13 Chapter 3 Automatic Determination of Hematoma Type Using Decision Trees 15 3.1 Introduction 15 3.2 Materials and Methods 16 3.2.1 Materials 16 3.2.2 Preprocessing 17 3.2.3 Lesion localization by multi-resolution thresholding 19 3.2.4 Recognition of the long and short axes and other features 21 3.2.5 Constructing the decision tree for diagnosing the type of hematoma 22 3.3 Results 23 3.3.1 Automatic hematoma Recognition 23 3.3.2 The discovered knowledge 25 3.4 Discussion 27 3.5 Summary 30 Chapter 4 Decision Trees for Hematoma Classification in Different Resolutions 31 4.1 Introduction 31 4.2 Materials and methods 32 4.2.1 Preprocessing 32 4.2.2 Lesion segmentation and feature extraction 34 4.2.3 Applying knowledge discovery techniques for data mining 36 4.3 Results 38 4.3.1 Feature extraction in different resolutions 38 4.3.2 Decision trees in different resolutions 39 4.4 Discussion 41 4.5 Summary 43 Chapter 5 A Multi-Resolution Binary Level Set Method for Image Segmentation 44 5.1 Introduction 44 5.2 Optimization based on binary level set method 45 5.3 Our multiresolution binary level set approach 49 5.4 Application to intracranial hematoma segmentation 52 5.5 Experimental results 57 5.6 Discussion 61 5.7 Summary 65 Chapter 6 Automatic Volumetry of Intracranial Hematomas from Raw Images 66 6.1 Introduction 66 6.2 Knowledge-driven segmentation of intracranial regions 67 6.3 Selection of hematoma slices based on connectivity 73 6.4 Intracranial hematoma segmentation 77 6.5 Materials and results 85 6.6 Discussion 93 6.7 Summary 94 Chapter 7 Automatic Measurement of the Midline Shift Using Symmetry and Curve Fitting 95 7.1 Introduction 95 7.2 Materials and methods 97 7.2.1 Notations 98 7.2.2 A parametric model of the deformed midline 100 7.2.3 Skull recognition and region designation 104 7.2.4 Finding the intact midline (iML) and aligning the image 106 7.2.5 Generating the map of one-dimensional symmetry 107 7.2.6 Determining the parameters using genetic algorithm 109 7.2.7 Evaluating the stability of the results 113 7.3 Results 116 7.4 Discussion 118 7.5 Summary 124 Chapter 8 Landmark-Based Measurement of the Midline Shift 125 8.1 Introduction 125 8.2 Knowledge-driven recognition of the frontal horns in low resolution 128 8.3 Segmentation of the frontal horn region 133 8.4 Recognition of the septum pellucidum using Hough transform 140 8.5 Results and evaluation 144 8.6 Summary 151 Chapter 9 Automatic Recognition of the Basal Cisterns Using Hough Transform 152 9.1 Introduction 152 9.1.1 Clinical relevance 152 9.1.2 Standardizing the evaluation of the BC 153 9.1.3 Applications of Hough transform in medical image analysis 154 9.1.4 Goal 156 9.2 Materials and methods 156 9.2.1 Image acquisition 156 9.2.2 Preprocessing 157 9.2.3 Our model of the basal cisterns 159 9.2.4 Detecting the abnormal basal cisterns 164 9.3 Results 165 9.4 Discussion 167 9.4.1 Advantages of our algorithm 167 9.4.2 Limitations of our algorithm 168 9.4.3 Potential applications of our algorithm 169 9.5 Summary 171 Chapter 10 Conclusion and Future Works 172 10.1 Conclusion 172 10.2 Limitations of current methodology 172 10.3 Automatic annotator and report generator 173 10.3.1 Integrating with clinical practice guidelines 175 10.4 Computational anatomy: midline markers at different tissue layers 176 REFERENCES 181 | |
| dc.language.iso | en | |
| dc.subject | 電腦輔助診斷 | zh_TW |
| dc.subject | 顱內血腫 | zh_TW |
| dc.subject | 中線偏移 | zh_TW |
| dc.subject | 決策樹 | zh_TW |
| dc.subject | 二值性等位集合法 | zh_TW |
| dc.subject | midline shift | en |
| dc.subject | computer-aided diagnosis | en |
| dc.subject | multi-resolution binary level set method | en |
| dc.subject | intracranial hematoma | en |
| dc.subject | decision tree | en |
| dc.title | 急性顱內血腫在電腦斷層影像中的電腦輔助診斷 | zh_TW |
| dc.title | Computer-Aided Diagnosis of Acute Intracranial Hematomas on Computed Tomographic Images | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 蔣以仁(I-Jen Chiang) | |
| dc.contributor.oralexamcommittee | 陳中明,林發暄,杜永光,廖弘源 | |
| dc.subject.keyword | 顱內血腫,電腦輔助診斷,決策樹,二值性等位集合法,中線偏移, | zh_TW |
| dc.subject.keyword | intracranial hematoma,computer-aided diagnosis,decision tree,multi-resolution binary level set method,midline shift, | en |
| dc.relation.page | 190 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2010-06-30 | |
| 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 未授權公開取用 | 5.51 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。