跳到主要內容

臺灣博碩士論文加值系統

(18.97.9.172) 您好!臺灣時間:2025/02/18 03:52
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:許益彰
研究生(外文):Yi-Jiang Shi
論文名稱:基因演算法用於超音波影像辦識
論文名稱(外文):Application of Genetic Algorithm for the Recognition of Ultrasonic Images
指導教授:江青芬陳志良陳志良引用關係
指導教授(外文):Ching-Fen JiangChih-Liang Chen
學位類別:碩士
校院名稱:義守大學
系所名稱:電子工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2000
畢業學年度:88
語文別:中文
中文關鍵詞:自動分類紋理特徵遮罩傳統特徵抽取法基因演算法價值函數
外文關鍵詞:auto-selectionLaws` feature maskmomentsgenetic algorithmcost funtion
相關次數:
  • 被引用被引用:0
  • 點閱點閱:495
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
基因演算法是一種仿照自然界演化特性的方法,利用物競天擇,適者生存的彼此競爭方式,及配合族群之間的交配,與自我突變, 以求在演化的過程中產生較好的個體。此法具有隨機與多點找尋的優點,並在許多研究中顯示此法所得結果好且穩定。
在本論文中,我們提出了一種可將超音波影像完全自動分類的方法。此方法可分為以下三個步驟:利用紋理特徵遮罩及傳統特徵抽取法先做特徵抽取,再以基因演算法從中篩選出所需的特徵(利用價值函數)。最後利用最小距離分類法或自我組織映射圖網路加以分類。
此方法可明確的將超音波影像分類。因此,以基因演算法為基礎的自動影像特徵抽取系統,將有助於提昇超音波影像的辨識率,以期有助於對超音波診斷的醫療發展。

The Genetic algorithm mimics the process of natural evolution, which the driving process for the emergence of complex and well-adapted organic structures. In the natural world, after computing with each other the fittest individuals survive and reproduce next generations. Genetic algorithms can search the optimal and stable solutions of the complex problems in diverse fields as the fittest individuals parallelly.
In this thesis, the method for auto-selection of the features of the ultrasonic images is proposed. The algorithm can be divided into the three steps: At first, features were of the original image including the texture features and the statistical features extracted by convolution of Laws’ Feature masks and calculation of the moments respectively. Then, main features were selected by Genetic Algorithm (using cost function). Finally, different tissues were classified by K-means clustering or Self-organizing feature maps.
The ultrasonic images were categorically segmented into several parts by the auto-classification with Genetic Algorithm. This auto-feature-selection system based on the genetic algorithm can improve the identification of ultrasonic images, therefore can assist the diagnose by the ultrasonic images.

第一章 緒論……………………………………………………1-1
1.1研究背景與目的………………………………………..1-1
1.2研究現況分析…………………………………………..1-2
1.3論文內容概述…………………………………………..1-6
第二章 超音波影像特徵與前處理概述……………………..2-1
2.1 超音波影像處理概述…………………………………….2-1
2.2 Laws’ Mask 特徵抽取法…………………………………2-3
2.3 傳統特徵抽取法─Moments……………………………..2-9
第三章 利用基因演算法自動篩選影像特徵………………..3-1
3.1 基因演算法……………………………………………….3-1
3.2 遺傳編碼………………………………………………….3-3
3.3 汰選……………………………………………………….3-9
3.4 交配……………………………………………………….3-11
3.5 突變……………………………………………………….3-13
第四章 影像的分類……………………………………………4-1
4.1 K-means 分類法………………………………………….4-1
4.2 SOM 分類法……………………………………………….4-5
第五章 結果與討論…………………………………………..5-1
5.1 基因演算法的參數設定……..………………………….5-1
5.2 利用基因演算法與人為篩選特徵影像的比較………….5-4
5.3 超音波影像的分類結果………………………………….5-6
5.3.1 K-means 的分類結果………………………………….5-6
5.3.2 SOM的分類結果………………………………………..5-11
第六章 結論與未來展望. ……………………………………6-1
6.1 結論……………………………………………………….6-1
6.2 未來展望………………………………………………….6-2
[1]Mitsuo Gen and Runwei Cheng “Genetic Algorithms and Engineering Design”, A Wiley-Interscience Publication, 1997.
[2]Goldberg. D. “Genetic Algorithms in search, Optimization and Machine Learning”, Addison-Wesley, Reading, MA, 1989.
[3]Hiller, F. and G. Lieberman, “Introduction to Mathematical Programming”, McGraw-Hill, New York, 1991.
[4]Kall. P. and S. W. Wallace. “Stochastic Programming”, John Wiley & Sons, Chichester, 1994.
[5]Karaman. M. Kutay, M. A., Bozdagi, G. “An Adaptive Speckle Suppression Filter for Medical Ultrasonic Imaging”, IEEE Trans. Med. Imag. m14(2): 283-292, 1995.
[6]Koo, J. I., And Park, S. B. “Speckle Recuction with Edge Preservation in Medical Ultrasonic Images Using a Homogeneous Region Growing Mean Filter”, Ultrasonic Imaging 13:211-237, 1991.
[7]Wagner, R. F., Smith, S. W., and Sandrik, J. M. “Statistics of Speckle in Ultrasound B-Scan”, IEEE Trans. Son. Ultrason., Vol. SU-30, no. 3, p156-163 May, 1983.
[8]Crawford, D. C., Bell, D. S., and Bamber, J. C. “Compensation for the Signal Processing Characteristics of Ultrasound B-MODE Scanners in Adaptive Speckle Reduction”, Ultrasound Med. Biol., 19:469-485, 1993.
[9]Mu-Long Chen and Ching-Fen Jiang “Recognition Of Ovarian Tumor By 2D ultrasonic Image”, Dept. of Electronic Engineering, I-Shou University Kaohsiung, Taiwan, R.O.C., January 1999.
[10]Shu-Chien Huang and Yung-Nien Sun “A Study Of Genetic Algorithms On Polygonal Approximation and Medical Image Analysis” In Dept. of Computer Science and Information Engineering, National Cheng Kung University Tainan, Taiwan, R.O.C. Dissertation for Doctor of Philosophy, June 1999.
[11]C. F. Jiang and M. L. Chen, “Segmentation of Ultrasonic Ovarian Images by Texture Features”, 20th Annual International Conf. of IEEE BMES, Hong Kong, pp185, 1998.
[12]William D. Richard and Constance G. Keen, “Automated Texture-Based Segmentation Of Ultrasound Images Of The Prostate“in Computerized Medical Imaging and Graphics, Vol. 20, No. 3, pp. 131-140, 1996.
[13]Hin Leong Tan, Saul B. Gelfand, and Edward J. Delp. Senior Member, IEEE “A Cost Minimization Approach to Edge Detection Using Simulated Annealing” IEEE transactions on pattern analysis and machine intelligence, vol. 14, no. 1, January 1991.
[14]Pon-Zen Chen, Chin-Hsing Chen and Chih-Ming Tsai “A Genetic Feature Selection For Texture Segmentation Using Wavelets” Dept. of Electronic Engineering, National Cheng Kung University Tainan, Taiwan, R.O.C. Thesis for Master of Science June 1998.
[15]Zhou, H. H. and J. J. Grefenstette, “Learning by analogy in genetic classifier system,” in Proc. 3rd Int. Conf. Genetic Algorithms, George Mason Univ., Arlington, VA, pp.291-297, 1989.
[16]Abdollah Homaifar, Charlene X. Qi and Steven H. Lai ”Constrained Optimization Via Genetic Algorithms” Simulation 62:4, 242-254, Simulation Councils, Inc. Printed in the United States of America, 1994.
[17]C. M. Wu, and Y. C. Chen, “Texture feature for classification of ultrasonic liver images”, IEEETrans. Med. Imaging, Vol. 11, No. 2, 141-152, 1992.
[18]Papoulis, A. “Probability, Rondam Variables, and Stochastic Processes, McGraw-Hill, New York”, 1965.
[19]Hu, M.K. “Visual pattern Recognition by Moment Invariants”, IRE Trans. Info. Theory, vol. IT-8, pp. 179-187, 1962.
[20]C. H. Chen “A topotraphical feature descriptor for gray scale images”, pp 313-317 CVGIP, 1997.
[21]Hin Leong Tan, Saul B. Gelfand, Member, IEEE, and Edward J. Delp. Senior Member, IEEE “A Cost Minimization Approach to Edge Detection Using Simulated Annealing”, IEEE transactions on pattern analysis and machine intelligence, vol. 14, no. 1, January 1991.
[23]S. Kirkpatrick, C. D. Gelatt, Jr., and M. P. Vecchi, “Optimization by Simulated annealing”, Sci., vol. 220, pp. 671-680, 1983.
[24]Hin Leong Tan, “Edge detection by cost minimization ”, Ph. D. dissertation, Purdue Univ., West Lafayette, IN, 1989.
[25]Hin Leong Tan, Saul B. Gelfand, Member, IEEE, and Edward J. Delp. Senior Member, IEEE “A Cost Minimization Approach to Edge Detection Using Simulated Annealing”, IEEE transactions on pattern analysis and machine intelligence, vol. 14, no. 1, January 1991.
[26]De jong. K., “An Analsis of the Behavior of a Class of Genetic Adaptive Systems.” Ph. D. thesis, University of Michigan, Ann Arbor, 1975.
[27]Fogel. D. and A. Ghozeil. “Using fitness distributions to design more efficient evolutionary computations”, in Fogel pp 11-19.
[28]C. F. Jiang and M. L. Chen, “A Comparative Study of Using Texture Features to Classify Ultrasonic Ovarian Images”, International Conf. Image and Vision Computing, Auckland, pp222-227, 1998.
[29]Rafael C. Gonzalez and Richard E. Woods “Digital Image Processing”, Addison-Wesley, 1993.
[30]Abhijit S. Pandya and Robert B. Macy, “ Pattern Recognition with Neural Networks in C++”, IEEE PRESS, 1995

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top