By Frances F. Yao (auth.), Xiaotie Deng, Ding-Zhu Du (eds.)

This ebook constitutes the refereed lawsuits of the sixteenth overseas Symposium on Algorithms and Computation, ISAAC 2005, held in Sanya, Hainan, China in December 2005.

The 112 revised complete papers awarded have been rigorously reviewed and chosen from 549 submissions. The papers are geared up in topical sections on computational geometry, computational optimization, graph drawing and graph algorithms, computational complexity, approximation algorithms, web algorithms, quantum computing and cryptography, facts constitution, computational biology, experimental set of rules mehodologies and on-line algorithms, randomized algorithms, parallel and allotted algorithms.

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Additional info for Algorithms and Computation: 16th International Symposium, ISAAC 2005, Sanya, Hainan, China, December 19-21, 2005. Proceedings

Sample text

The following lemma is a key to our algorithm. Lemma 1. Any closed set C = ∅ in G specifies l feasible net surfaces in G whose total cost differs from that of C by a fixed value cl (V ). Next, we argue that any l feasible net surfaces, NS = {N 1 , N 2 , . . , N l }, in G correspond to a closed set C = ∅ in G . Based on the construction of G , every vertex vk on the net N i corresponds to a vertex vki in Gi (vki is not a deficient vertex). We construct a closed set Ci = ∅ in Gi for each net N i , as follows.

The simultaneous detection of multiple inter-related surfaces has been studied by the medical image analysis community for a long time. For the 2-D case, there are several satisfactory results [11, 1, 14]. However, little work has been done on the three and higher dimensional cases. Previous attempts [12, 3] on extending graph-search based segmentation methods for the 2-D case to identifying even a single optimal surface in 3-D medical images either made the methods computationally intractable or traded their ability to achieve global optima for computational efficiency.

3 seconds. An accuracy assessment on images of physical phantom tubes revealed that the overall signed errors for the inner and outer diameters derived from the tube boundaries were (mean ± 26 X. Wu et al. 35%, respectively. Our LNS approach was tested on segmenting both the inner and outer airway wall surfaces in CT images, in which outer wall surfaces are very difficult to detect due to their blurred and discontinuous appearance and the presence of adjacent blood vessels. 6mm3. The currently used 2-D dynamic programming method is unsuitable for the segmentation of the outer airway wall.

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