自動輪廓線辨識機制是由使用者給定一張切片影像及一條初始、精準的輪廓線，從一系列的影像中，找出每一張影像相同器官的輪廓線，以大幅減少使用者繪製輪廓線的時間。
本機制採用的動態輪廓線模型是以離散動態輪廓線模型為基礎加以改良，改善傳統模型準確度及效率方面等不足之處。在新的模型中對影像前處理進行增強：加強物件的特徵、減弱雜訊的影響，對變形的各種錯誤進行偵測、修正，並利用輪廓線評估系統評估最後變形的結果，修正變形不佳之處，增加器官邊緣的準確性；另外，機制中也加入器官模型的器官形狀、大小等相關參考資訊來偵測器官輪廓的走勢與變化，對動態輪廓線模型做應對的調整使變形結果更趨完美。

Automatic Contour Recognition Mechanism is to start out with a given image and an initial contour that circles the organ of interest and extract the organ contour from a stack of images. It is used to reduce the effort of drawing contours by users.
The mechanism used is based on the discrete dynamic contour model and is improved in terms of the accuracy and the performance. In the mechanism, we enhance the image pre-processing, detect and correct various deformation errors, and evaluate the final deformation result by the Contour Evaluation System. The mechanism also adds the information of the organ model such as organ shape, organ size…etc, to estimate the shape and the change of the organ contour, adjusts the active contour model, and improved the deformation result.

[1]: Bezdek, J.C., "A Geometric Approach to Edge Detection," IEEE Transaction On Fuzzy systems,Vol.6, No.1, February 1998
[2]: Canny, J. F., "A computational approach to edge detection," IEEE Trans, Pattern Analysis and Machine Intelligence, Vol. 9, pp. 679-698, Nov. 1986
[3]: Cohen, L. D., "Note : On Active Contour Models and Ballons," CVGIP: Image Understanding, Vol.53, No. 2, March, pp. 211-218, 1991
[4]: Deng, Y., Kenney, S., Moore, M.S., and Manjunath, B. S., "Peer group filtering and perceptual color image quantization," Proc. IEEE International Symposium on Circuits and Systems VLSI , (ISCAS'99), Orlando, FL, vol 4, pp.21-4 , June 1999
[5]: Gonzalez, R. C., and Woods, R. E., "Digital Image Processing," Second Edition, Prentice-Hall Inc., 2002
[6]: Hsu, H. C., "The Applications of the Automatic Contour Recognition Mechanism on the Medical Images," National Cheng Kung University, 2005
[7]: Hsu, H. L., "The Application of the Contour Evaluation System on the Medical Images," National Cheng Kung University, 2006
[8]: Kass, M., Witkin, A., Terzopoulos, D., "Snake: Active Contour Models," International Journal of Computer Vision, pp.321-331, 1988
[9]: Kenney, C., Deng, Y., Manjunath, B. S., Hewer, G., "Peer group image enhancement," IEEE Trans. Image Processing, vol. 10, pp. 326--334, Feb. 2001
[10]: Lin, Y. J., "An Active Contour Model and Its Applications in Medical 3D Imaging," National Cheng Kung University, 2004
[11]: Lobregt, S., Viergever, M. A., "A Discrete Dynamic Contour Model," IEEE Trans. on Medical Imaging, Vol. 14, No. 1, pp. 12-24, Mar. 1995
[12]: Miller, J. V., Breen, D. E., Lorensen, W.E., OBara, R.M., Wozny, M.J., "Geometrically Deformed Models: A Method for Extracting Closed Geometric Models from Volume Data," Computer Graphics, Vol. 25, No. 4, pp. 217 –226, July 1991
[13]: Ruzon, M. A., Tomasi, C., "Color Edge Detection with the Compass Operator," cvpr, p. 2160, 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Volume 2, 1999
[14]: Ruzon, M. A., Tomasi, C., "Corner Detection in Textured Color Images," In 7th IEEE International Conference on Computer Vision, Kerkyra, Greece, V. 2, pp. 1039-1045, September 1999
[15]: Ruzon, M. A., Tomasi, C., "Edge, Junction, and Corner Detection Using Color Distributions," In IEEE Transactions on Pattern Analysis and Machine Intelligence, V. 23, No. 11, pp. 1281-1295, November 2001
[16]: Tagare, H.D., "Deformable 2-D Template Matching Using Orthogonal Curves," IEEE Transactions On Medical Imaging, Vol. 16, No.1, pp. 108-117, February 1997
[17]: Yezzi, A., Jr., Staranad kichenassamy, and Arun Kumar, Peter Olver, and Allen Tannenbaum, "A Geometric Snake Model for Segmentation of Medical Imagery," IEEE Transactions on Medical Imaging, Vol. 16, No.2, April 1997
[18]: Yulle, A. L., Cohen, D. S., Hallinan, P. W., "Feature extraction from faces using deformable templates," Proceedings of Computer Vision and Pattern recognition, San Diego, June 1989