一种基于相对模糊连通度的交互式序列图像快速分割算法

田春娜; 高新波; 哈力旦.A

一种基于相对模糊连通度的交互式序列图像快速分割算法

田春娜^{①;高新波},
高新波,
哈力旦.A

计量
- 文章访问数: 2253
- HTML全文浏览量: 112
- PDF下载量: 728
- 被引次数: 0
出版历程
- 收稿日期: 2004-05-08
- 修回日期: 2004-08-23
- 刊出日期: 2005-10-19

A Fast Interactive Segmentation Algorithm of Image Sequences Based on Relative Fuzzy Connectedness

Tian Chun-na^{①;高新波},
Gao Xin-bo,
Halidan.A

摘要

摘要: 在基于相对模糊连通度的交互式图像分割方法的基础上提出了一种序列图像分割的快速算法。对于单幅图像，新算法在保持分割精度的同时，运行速度提高了3倍。作为该算法的扩展，一方面将原算法中单目标分割推广到多目标分割，另一方面将单帧图像的分割推广到序列图像的分割，实现了复杂背景下多目标的图像分割和图像序列的批处理分割。并进行了分割结果的后处理，提取出目标的单像素宽度的光滑边缘。用人造图像和实际的医学图像和图像序列所做的测试实验取得了令人满意的分割结果。
- 模糊连通度;交互式图像分割;序列图像分割;多目标分割
Abstract: A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the existing algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex background and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.

HTML全文

参考文献(1)

Pal N R, Pal S K. A review of image segmentation techniques[J].Pattern Recognition.1993, 26(9):1277-1294[2]罗希平, 田捷, 诸葛婴等. 图像分割方法综述. 模式识别与人工智能, 1999, 12(3): 300-312.[3]Falco A X, Udupa J K, Samarasekera S, Sharma S. User-steered image segmentation paradigms: Live Wire and Live Lane[J].Graphical Models and Image Processing.1998, 60(4):233-260[4]李培华, 张田文. 主动轮廓线模型(蛇模型)综述. 软件学报, 2000, 11(6): 751- 757.[5]高新波, 雷云, 姬红兵. 一种改进的Live-Wire交互式图像分割算法. 系统工程与电子技术, 2003, 25(8): 915-917.[6]Canny J. A computational approach to edge detection. IEEE Trans. on PAM, 1986, 8(6): 679-698.[7]Udupa J K, Samarasekera S. Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation[J].Graphical Models and Image Processing.1996, 58(3):246-261[8]Saha P K, Udupa J K, Odhner D. Scale-based fuzzy connected image segmentation: Theory, algorithms, and validation[J].Computer Vision and Image Understanding.2000, 77(2):145-174[9]Carvalho B M, Gau C J, Herman G T, Kong T Y. Algorithms for fuzzy segmentation[J].Pattern Analysis Applications.1999, 2(1):73-81[10]Lin Yao, Tian Jie, He Huiguang. Image segmentation via fuzzy object extraction and edge detection and its medical application.[11]Journal of X-Ray Science and Technology, 2002, 10(1): 95-106.[12]Udupa J K, Saha P K, Lotufo R A. Fuzzy connected object definition in images with respect to co-objects. Medical Imaging,[13]99, SPIE Proceedings, San Diego, CA, 1999, 236-245.[14]Saha P K, Udupa J K. Iterative relative fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation. Mathematical Methods in Biomedical Image Analysis, 2000, Proceedings of IEEE, Hilton Head, South Carolina, 2000: 28-35.[15]Udupa J K, Saha P K, Lotufo R A. Relative fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2002, 24(11):1485-1500[16]Udupa J K, Saha P K. Fuzzy connectedness and image segmentation. Parallel and Large-Data Visualization and Graphics, 2003, Proceedings of the IEEE, Seattle, WA, USA, 2003: 1649-1669.[17]陈士金, 汤漾平, 邓勇. 基于链码的轮廓跟踪技术在二值图像中的应用. 华中理工大学学报, 1998, 26(12): 26-28.[18]Pavlidis T. Algorithms for Graphics and Image Processing. Rockville, MD, USA: Computer Science Press, 1982: 199-273.

施引文献

资源附件(0)

访问统计