研究生: |
楊孟銓 Meng-Cyue Yang |
---|---|
論文名稱: |
自動化計算腰椎間盤突出患者椎管狹窄程度與手術準則關係之研究 Automatically Calculate the Degree of Spinal Stenosis in Patients with Lumbar Disc Herniation and Study on the Relationship Between Criteria of Surgery Treatment |
指導教授: |
郭中豐
Chung-Feng Kuo |
口試委員: |
劉林肯
Lincoln Liu 黃昌群 Chang-Chiun Huang 邱智瑋 Chih-Wei Chiu |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 材料科學與工程系 Department of Materials Science and Engineering |
論文出版年: | 2018 |
畢業學年度: | 106 |
語文別: | 中文 |
論文頁數: | 139 |
中文關鍵詞: | 椎間盤突出 、椎管狹窄 、影像處理 、電腦輔助偵測 、區域限制型主動輪廓法 、支持向量機 、三維重建 |
外文關鍵詞: | Disc Herniation, Spinal Stenosis, Image Processing, Computer Aided Detection, Improved Active Contour Method, Support Vector Machine, 3D-reconstruction |
相關次數: | 點閱:215 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
腰椎間盤突出症會造成椎管狹窄,是成年人神經根損傷最常見的原因。目前治療椎間盤突出症的方式主要分為手術治療及保守治療,然而是否需要進行手術治療並沒有一定的標準,文獻指出進行保守治療六週後病患症狀沒有明顯好轉,臨床醫師將建議病患進行手術治療。為了避免評估療效造成病患時間以及醫療資源的浪費,本研究提出一套自動偵測突出椎間盤病計算狹窄程度的系統,運用於腰部T2加權以及脂肪訊號抑制之核磁共振影像上,過程分別是影像前處理、椎間盤提取、突出判斷、椎管區域提取、重建與檢視。因為椎間盤的灰階值與肌肉、軟骨等組織非常相近,在椎間盤提取的部分提出了區域型主動輪廓法,運用限制圈選邊界的方式以得到更加精準的椎間盤輪廓。為了快速判斷出突出椎間盤,本研究使用支持向量機對椎間盤紋理與平均灰度值特徵進行分類。因為脂肪訊號抑制核磁共振影像中水分呈現白色,利用此特性迅速的將椎管提取出來,圈選突出的區域,得到椎管與椎間盤突出部位後進行三維重建。
本研究採用532位椎間盤突出病患的核磁共振影像進行實驗與評估,所提出的系統能自動計算狹窄程度並進行三維重建,每個樣本經過影像濾波、椎間盤圈選、突出辨識、椎管提取以及三維重建後,平均需要55秒進行計算,系統的準確率為96.42%。本研究之系統能夠輸出腰椎間盤突出部位三維重建模型以及椎管狹窄程度的量化數值,有助於臨床醫師的診斷與後續療程規劃。
Lumbar disc herniation will causes spinal stenosis, and it’s also the most common cause of nerve root injury in adults. At present, the methods for treating disc herniation are mainly divided into surgical treatment, and conservative treatment. However, there are no definite criteria for assessing whether or not it is necessary to perform surgery, in general, there is no obvious improvement after six weeks of conservative treatment. The medical staff will recommend the patient for surgical treatment. In order to avoid wasting patient’s time and medical resources while evaluation the efficacy of conservative treatment, this research proposes a set of image processing methods applied to spinal canal and intervertebral disc herniation in lumbar MRI image for calculating the degree of spinal and three-dimensional reconstruction. The process are image preprocessing, disc extraction, herniated disc judgment, spinal region extraction and three-dimensional reconstruction. Because the gray value of the disc is very similar to the surrounding tissue, an improved active contour method is proposed in the disc extraction part to obtain a more accurate disc contour. In order to quickly determine the herniated intervertebral discs, the support vector machine was used to classify the features of intervertebral discs such as texture features and average gray values. Because the moisture in the MR image of fat signal suppression is white, this feature can be used to quickly extract the spinal canal and find out the prominent areas to obtain the position of the spinal canal and disc herniation and commencing three-dimensional reconstruction.
In this study, 532 MR images of patients with disc herniation were examined and evaluated. The proposed system can automatically calculate the degree of stenosis and perform three-dimensional reconstruction, each sample takes 55 seconds to calculate, the accuracy of the system is 96.42%. The system of the this study can output three-dimensional reconstruction models and quantified values of spinal stenosis degree, which is helpful for the diagnosis of clinicians.
[1] Rasheed, S. M. (2015). Results and predictive factors for single level L4-5, and L5-S1 disc herniation surgery. Al-Kindy College Medical Journal, 11(2), 55-63.
[2] Frymoyer, J. W. (1988). Back pain and sciatica. New England Journal of Medicine, 318(5), 291-300.
[3] Koes, B. W., Van Tulder, M. W., & Thomas, S. (2006). Diagnosis and treatment of low back pain. BMJ: British Medical Journal, 332(7555), 1430.
[4] Van Der Windt, D. A., Simons, E., Riphagen, I. I., Ammendolia, C., Verhagen, A. P., Laslett, M., & Aertgeerts, B. (2008). Physical examination for lumbar radiculopathy due to disc herniation in patients with low-back pain (Protocol).
[5] Taylor, V. M., Deyo, R. A., Cherkin, D. C., & Kreuter, W. (1994). Low back pain hospitalization. Recent United States trends and regional variations. Spine, 19(11), 1207-12.
[6] Fairbank, J. C., & Pynsent, P. B. (2000). The Oswestry disability index. Spine, 25(22), 2940-2953.
[7] Alentado, V. J., Lubelski, D., Steinmetz, M. P., Benzel, E. C., & Mroz, T. E. (2014). Optimal duration of conservative management prior to surgery for cervical and lumbar radiculopathy: a literature review. Global spine journal, 4(4), 279-286.
[8] Konstantinou, K., & Dunn, K. M. (2008). Sciatica: review of epidemiological studies and prevalence estimates. Spine, 33(22), 2464-2472.
[9] Raja’S, A., Corso, J. J., Chaudhary, V., & Dhillon, G. (2010). Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI. International journal of computer assisted radiology and surgery, 5(3), 287-293.
[10] Jamaludin, A., Kadir, T., & Zisserman, A. (2017). SpineNet: Automated classification and evidence visualization in spinal MRIs. Medical image analysis, 41, 63-73.
[11] Al Kafri, A. S., Sudirman, S., Hussain, A. J., Fergus, P., Al-Jumeily, D., Al Smadi, H., & Bashtawi, M. (2017, June). Detecting the Disc Herniation in Segmented Lumbar Spine MR Image Using Centroid Distance Function. In Developments in eSystems Engineering (DeSE), 2017 10th International Conference on (pp. 9-13). IEEE.
[12] Li, S., Du, Y., & Liu, H. C. (2016, October). Research and implementation of spinal MRI image segmentation algorithm. In Computer and Communications (ICCC), 2016 2nd IEEE International Conference on (pp. 653-656). IEEE.
[13] Wu, M. C., Kuo, Y. L., Chen, C. W., Fang, C. A., Chin, C. L., Tsai, H. H., Wei, J. C. C. (2014). Degenerative disc segmentation and diagnosis technology using important features from MRI of spine in images. Biomedical Engineering: Applications, Basis and Communications, 26(04), 1440008.
[14] Beulah, A., & Sharmila, T. S. (2017, January). Em algorithm based intervertebral disc segmentation on mr images. In Computer, Communication and Signal Processing (ICCCSP), 2017 International Conference on (pp. 1-6). IEEE.
[15] Bampis, C. G., Bovik, A. C., Markey, M. K., & Webb, K. M. (2016, March). Segmentation and extraction of the spinal canal in sagittal MR images. In Image Analysis and Interpretation (SSIAI), 2016 IEEE Southwest Symposium on (pp. 5-8). IEEE.
[16] Beulah, A., & Sharmila, T. S. (2016, July). Classification of Intervertebral Disc on Lumbar MR Images using SVM. In Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on (pp. 293-297). IEEE.
[17] Ghosh, S., Raja'S, A., Chaudhary, V., & Dhillon, G. (2011, March). Computer-aided diagnosis for lumbar mri using heterogeneous classifiers. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on (pp. 1179-1182). IEEE.
[18] Oktay, A. B., Albayrak, N. B., & Akgul, Y. S. (2014). Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images. Computerized Medical Imaging and Graphics, 38(7), 613-619.
[19] Steurer, J., Roner, S., Gnannt, R., & Hodler, J. (2011). Quantitative radiologic criteria for the diagnosis of lumbar spinal stenosis: a systematic literature review. BMC musculoskeletal disorders, 12(1), 175.
[20] Geisser, M. E., Haig, A. J., Tong, H. C., Yamakawa, K. S., Quint, D. J., Hoff, J. T., & Phalke, V. V. (2007). Spinal canal size and clinical symptoms among persons diagnosed with lumbar spinal stenosis. The Clinical journal of pain, 23(9), 780-785.
[21] Lohman, C. M., Tallroth, K., Kettunen, J. A., & Lindgren, K. A. (2006). Comparison of radiologic signs and clinical symptoms of spinal stenosis. Spine, 31(16), 1834-1840.
[22] Lurie, J. D., Moses, R. A., Tosteson, A. N., Tosteson, T. D., Carragee, E. J., Carrino, J. A., & Herzog, R. J. (2013). Magnetic resonance imaging predictors of surgical outcome in patients with lumbar intervertebral disc herniation. Spine, 38(14), 1216.
[23] Mozley, P. D., Schwartz, L. H., Bendtsen, C., Zhao, B., Petrick, N., & Buckler, A. J. (2010). Change in lung tumor volume as a biomarker of treatment response: a critical review of the evidence. Annals of oncology, 21(9), 1751-1755.
[24] Frymoyer, J. W. (1988). Back pain and sciatica. New England Journal of Medicine, 318(5), 291-300.
[25] Hall, M. J., DeFrances, C. J., Williams, S. N., Golosinskiy, A., & Schwartzman, A. (2010). National hospital discharge survey: 2007 summary (pp. 1-20). US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.
[26] Nowakowski, A., Kubaszewski, L., & Kaczmarczyk, J. (2007). Lumbar disc herniation. Chirurgia narzadow ruchu i ortopedia polska, 72(2), 95-97.
[27] Kurtz, S. M., & Edidin, A. (2006). Spine technology handbook. Elsevier.
[28] Marchand, F., & Ahmed, A. M. (1990). Investigation of the laminate structure of lumbar disc anulus fibrosus. Spine, 15(5), 402-410.
[29] Orendáčová, J., Čı́žková, D., Kafka, J., Lukáčová, N., Maršala, M., Šulla, I., & Katsube, N. (2001). Cauda equina syndrome. Progress in neurobiology, 64(6), 613-637.
[30] Fardon, D. F., & Milette, P. C. (2001). Nomenclature and classification of lumbar disc pathology: recommendations of the combined task forces of the North American Spine Society, American Society of Spine Radiology, and American Society of Neuroradiology. Spine, 26(5), E93-E113.
[31] Postacchini, F. (1998). Lumbar disc herniation. Springer Science & Business Media.
[32] Mysliwiec, L. W., Cholewicki, J., Winkelpleck, M. D., & Eis, G. P. (2010). MSU classification for herniated lumbar discs on MRI: toward developing objective criteria for surgical selection. European Spine Journal, 19(7), 1087-1093.
[33] Spangfort, E. V. (1972). The lumbar disc herniation: a computer-aided analysis of 2,504 operations. Acta Orthopaedica Scandinavica, 43(sup142), 1-99.
[34] acobs, W. C., van Tulder, M., Arts, M., Rubinstein, S. M., van Middelkoop, M., Ostelo, R., & Peul, W. C. (2011). Surgery versus conservative management of sciatica due to a lumbar herniated disc: a systematic review. European Spine Journal, 20(4), 513-522.
[35] Lurie, J. D., Tosteson, T. D., Tosteson, A. N., Zhao, W., Morgan, T. S., Abdu, W. A., & Weinstein, J. N. (2014). Surgical versus non-operative treatment for lumbar disc herniation: eight-year results for the Spine Patient Outcomes Research Trial (SPORT). Spine, 39(1), 3.
[36] Gugliotta, M., da Costa, B. R., Dabis, E., Theiler, R., Jüni, P., Reichenbach, S., & Hasler, P. (2016). Surgical versus conservative treatment for lumbar disc herniation: a prospective cohort study. BMJ open, 6(12), e012938.
[37] Modic, M. T., Masaryk, T., Boumphrey, F., Goormastic, M., & Bell, G. (1986). Lumbar herniated disk disease and canal stenosis: prospective evaluation by surface coil MR, CT, and myelography. American Journal of Roentgenology, 147(4), 757-765.
[38] Lim, J. S. (1990). Two-dimensional signal and image processing. Englewood Cliffs, NJ, Prentice Hall, 1990, 710 p.
[39] Reza, A. M. (2004). Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of VLSI signal processing systems for signal, image and video technology, 38(1), 35-44.
[40] Jin, L., Wei, W. Y., Lei, W., Jie, W., & Da Da, W. (2011, August). Industrial X-ray image enhancement algorithm based on adaptive histogram and wavelet. In Strategic Technology (IFOST), 2011 6th International Forum on (Vol. 2, pp. 836-839). IEEE.
[41] Yan, H., Zuo, Y., Chen, Y., & Chen, Y. (2016, November). Evaluation of the morphology structure of meibomian glands based on mask dodging method. In Infrared Technology and Applications, and Robot Sensing and Advanced Control (Vol. 10157, p. 101573H). International Society for Optics and Photonics.
[42] Gao, W., Zhang, X., Yang, L., & Liu, H. (2010, July). An improved Sobel edge detection. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on (Vol. 5, pp. 67-71). IEEE.
[43] Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
[44] Forgy, E. W. (1965). Cluster analysis of multivariate data: efficiency versus interpretability of classifications. biometrics, 21, 768-769.
[45] Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters.
[46] Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203.
[47] Borgefors, G. (1986). Distance transformations in digital images. Computer vision, graphics, and image processing, 34(3), 344-371.
[48] Strand, R., & Normand, N. (2012). Distance transform computation for digital distance functions. Theoretical Computer Science, 448, 80-93.
[49] Vincent, L., & Soille, P. (1991). Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis & Machine Intelligence, (6), 583-598.
[50] Guo, Z., & Hall, R. W. (1989). Parallel thinning with two-subiteration algorithms. Communications of the ACM, 32(3), 359-373.
[51] Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International journal of computer vision, 1(4), 321-331.
[52] Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 3(6), 610-621.
[53] Bellman, R. (1956). Dynamic programming and Lagrange multipliers. Proceedings of the National Academy of Sciences, 42(10), 767-769.
[54] Seni, G., & Elder, J. F. (2010). Ensemble methods in data mining: improving accuracy through combining predictions. Synthesis Lectures on Data Mining and Knowledge Discovery, 2(1), 1-126.
[55] Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-validation. In Encyclopedia of database systems (pp. 532-538). Springer, Boston, MA.
[56] Lorensen, W. E., & Cline, H. E. (1987, August). Marching cubes: A high resolution 3D surface construction algorithm. In ACM siggraph computer graphics (Vol. 21, No. 4, pp. 163-169). ACM.
[57] Prescott, P. (2004). Student's t‐Tests. Encyclopedia of Statistical Sciences, 13.
[58] Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29-36.
[59] Hajian-Tilaki, K. (2013). Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal medicine, 4(2), 627.
[60] Van Erkel, A. R., & Peter, M. (1998). Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. European Journal of radiology, 27(2), 88-94.
[61] Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3(1), 32-35