Paper
27 February 1996 Improved adaptive boundary tracing using 2D dynamic programming
He Wang, Tian-ge Zhuang, Dazong Jiang, Wan-Yu Liu, Isabelle E. Magnin, Gerard Gimenez
Author Affiliations +
Proceedings Volume 2727, Visual Communications and Image Processing '96; (1996) https://doi.org/10.1117/12.233232
Event: Visual Communications and Image Processing '96, 1996, Orlando, FL, United States
Abstract
Edge detection is one of the most important steps in the process of object recognition and 3D display of medical images. Although lots of techniques have been presented to extract edges, few of them can give rapid results with thinness, connectedness, closedness as well as optimization. Most works are still done manually. In this paper, after a review of the previous works on edge detection, an improved dynamic programing (DP)-based algorithm is proposed. On the basis of the initial cost matrix provided by gradient information, local window technique is introduced into the traditional 2D DP to speed up the algorithm with the optimal parameters being determined adaptively in the window. As the local window moves forward along the boundary, the contour of a defined object will be traced segment by segment, and finally an optimal and connected result is acquired. From the experiments on synthetic images with and without noise, the quantitative evaluation of the performance and computational complexity of the algorithm are studied. In the practical application, medical images are processed to show that the improved DP approach can achieve a fast and ideal edge tracing operation.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
He Wang, Tian-ge Zhuang, Dazong Jiang, Wan-Yu Liu, Isabelle E. Magnin, and Gerard Gimenez "Improved adaptive boundary tracing using 2D dynamic programming", Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); https://doi.org/10.1117/12.233232
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KEYWORDS
Edge detection

Image segmentation

Medical imaging

Detection and tracking algorithms

Image processing

Algorithm development

Computer programming

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