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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 郭柏齡 | |
dc.contributor.author | Hsueh-Fu Shih | en |
dc.contributor.author | 施學甫 | zh_TW |
dc.date.accessioned | 2021-05-13T06:41:00Z | - |
dc.date.available | 2018-07-07 | |
dc.date.available | 2021-05-13T06:41:00Z | - |
dc.date.copyright | 2017-07-07 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-07-06 | |
dc.identifier.citation | [1] Sen,C. K., Gordillo, G. M., Roy, S., Kirsner, R., Lambert, L., Hunt, T. K., ... & Longaker, M. T. (2009). Human skin wounds: a major and snowballing threat to public health and the economy. Wound Repair and Regeneration, 17(6), 763-771.
[2] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4), 541-551. [3] Song, B., & Sacan, A. (2012, October). Automated wound identification system based on image segmentation and Artificial Neural Networks. In Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on (pp. 1-4). IEEE. [4] Kolesnik, M., & Fexa, A. (2006, June). How robust is the SVM wound segmentation?. In Proceedings of the 7th Nordic Signal Processing Symposium-NORSIG 2006 (pp. 50-53). IEEE. [5] Gonzalez, R. C., & Woods, R. E. (2008). Digital image processing. Nueva Jersey. [6] Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), 679-698. [7]Mohsen, F. M., Hadhoud, M. M., & Amin, K. (2011). A new optimization-based image segmentation method by particle swarm optimization. International Journal of Advanced Computer Science and Applications, Special Issue on Image Processing and Analysis, 10-18. [8] Zhao, X., Lee, M. E., & Kim, S. H. (2008, March). Improved image segmentation method based on optimized threshold using genetic algorithm. In2008 IEEE/ACS International Conference on Computer Systems and Applications (pp. 921-922). IEEE. [9] Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press. [10] Zhang, H., Fritts, J. E., & Goldman, S. A. (2008). Image segmentation evaluation: A survey of unsupervised methods. computer vision and image understanding, 110(2), 260-280. [11] Pal, N. R., & Bhandari, D. (1993). Image thresholding: some new techniques. Signal processing, 33(2), 139-158. [12] Zhang, H., Fritts, J. E., & Goldman, S. A. (2003, December). An entropy-based objective evaluation method for image segmentation. In Electronic Imaging 2004 (pp. 38-49). International Society for Optics and Photonics. [13] Chen, H. C., & Wang, S. J. (2004, May). The use of visible color difference in the quantitative evaluation of color image segmentation. In Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP'04). IEEE International Conference on (Vol. 3, pp. iii-593). IEEE. [14] Albiol, A., Torres, L., & Delp, E. J. (2001, October). Optimum color spaces for skin detection. In ICIP (1) (pp. 122-124). [15]Kakumanu, P., Makrogiannis, S., & Bourbakis, N. (2007). A survey of skin-color modeling and detection methods. Pattern recognition, 40(3), 1106-1122. [16] Fossati, L., Lanzi, P. L., Sastry, K., Goldberg, D. E., & Gomez, O. (2007, September). A simple real-coded extended compact genetic algorithm. In 2007 IEEE Congress on Evolutionary Computation (pp. 342-348). IEEE. [17] Borsotti, M., Campadelli, P., & Schettini, R. (1998). Quantitative evaluation of color image segmentation results. Pattern recognition letters, 19(8), 741-747. [18] Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992-1007. [19] Schaffer, J. D., & Eshelman, L. J. (1991, July). On Crossover as an Evolutionarily Viable Strategy. In ICGA (Vol. 91, pp. 61-68). | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2496 | - |
dc.description.abstract | 在病人接受手術之後,傷口的術後照護對病人的健康狀況佔了很重要的因素,往往都需要花費數日甚至數週的時間,在病房等待傷口穩定後才能出院,需要醫護人力來注意傷口四周的發炎感染跡象。
隨著影像辨識以及機器學習技術的演進,許多近來的研究提出了類似的解法,有透過大量資料透過卷積類神經網路進行學習,或者使用高解析度及紅外線攝影機進行精確攝影定位的傷口分析,但在我們的使用場景中,我們希望病人及醫護端使用者可以使用較少的資料運算資源以及不需高門檻的硬體設備,便能得到即時的傷口狀態評估。 本論文計畫開發一個演算法與系統使術後傷口的照護與發炎感染判斷能夠自動化,以統計及電腦視覺的方式來評估傷口是否有發生感染,並且與台大醫院遠距中心合作,整合台大醫院資訊系統,承接台大醫院-心臟節律器傷口自動判讀照護計畫,此研究計畫開發並改進傷口切割及判讀的演算法,使其更適用於目前的使用場景,建立傷口照護照片的雲端資料庫,建立對應行動裝置的APP以利病人及護理人員使用,並透過所接收的資料進行整體機器學習演算法的改良。 本篇論文著重於傷口影像的切割定位及其最佳化演算法,使用了基於灰階色彩空間強度進行動態閥值決定,以及引進不同的最佳化方法,包括基因演算法等來進行切割結果的最佳化,並提出了一個評估傷口切割效率的評估函數。透過與台大醫院外科部合作的手術傷口資訊進行驗證。 傷口切割定位演算法在台大醫院心臟外科部提供的心臟節律器傷口資料上達到了 75.7%的準確度,透過基因演算法及評估函數的最佳化後更達到了94.3%的切割效率。 | zh_TW |
dc.description.abstract | After the surgery being taken, the after care of the surgical wound has a great impact toward the patients’ prognosis. It’s often takes few days even few weeks for the wound to stabilize. It’s is a great cost of health care and nursing resources.
The advance of image process and machine learning improves the accuracy of wound assessment and analysis and there are some recent works started on this field of wound analysis. In our tele-health scenario, we hope the user can use their mobile device to obtain an accurate result without using high-end camera. In this literature, we proposed an image segmentation algorithm based on edge detection and Hough transform. We further developed an optimization method based on unsupervised image segmentation evaluation and genetic algorithm. The result was evaluated by the image provided by NTUH, division of surgery. We also implemented an analysis system cooperate with NTUH telehealth center, which has been used on pacemaker implantation patient. The result of performing this segmentation algorithm on the data set provided by NTUH, Division of cardiovascular surgery, achieve the accuracy of 75.7%, after the optimization of genetic algorithm it achieves 94.3%. | en |
dc.description.provenance | Made available in DSpace on 2021-05-13T06:41:00Z (GMT). No. of bitstreams: 1 ntu-106-R03945026-1.pdf: 1528505 bytes, checksum: a83212b30e4159c3f1f8b85b30ee2a49 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 中文摘要 ii
Abstract iv Content vi Figures viii Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Image Segmentation 2 1.3 Genetic algorithm 4 1.4 Unsupervised Segmentation Evaluation 5 Chapter 2 Method 8 2.1 Segmentation Algorithm Pipeline 8 2.2 Optimize the segmentation 17 2.3 Optimization and Genetic algorithm 22 Chapter 3 System architecture 30 Chapter 4 Result 32 4.1 Experiment environment 32 4.2 Execution time 32 4.3 Segmentation Result 33 4.4 Result of GA Optimization 35 Chapter 5 Conclusion 37 Chapter 6 Future work 38 Chapter 7 Reference 39 | |
dc.language.iso | en | |
dc.title | 基於基因演算法及非監督式評估傷口影像的切割與最佳化 | zh_TW |
dc.title | Segmentation of wound image and optimization based on genetic algorithm and unsupervised evaluation. | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 賴飛羆 | |
dc.contributor.oralexamcommittee | 汪大暉,許凱平,蔡坤霖 | |
dc.subject.keyword | 影像切割,電腦輔助診斷,最佳化,基因演算法,傷口判讀, | zh_TW |
dc.subject.keyword | Image segmentation,Computer Aided Diagnosis,Optimization,Genetic algorithm,Wound analysis, | en |
dc.relation.page | 42 | |
dc.identifier.doi | 10.6342/NTU201701316 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2017-07-06 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
顯示於系所單位: | 生醫電子與資訊學研究所 |
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