Fast automatic algorithm for bifurcation detection in vascular CTA scans

2012 ◽  
Author(s):  
Matthias Brozio ◽  
Vladlena Gorbunova ◽  
Christian Godenschwager ◽  
Thomas Beck ◽  
Dominik Bernhardt
Keyword(s):  
Bifurcation Detection ◽  
Automatic Algorithm

POLYGONAL APPROXIMATION OF DIGITAL CURVE BASED ON REVERSE ENGINEERING CONCEPT

2013 ◽  
Vol 13 (04) ◽  
pp. 1350017 ◽  
Author(s):  
KUMAR S. RAY ◽  
BIMAL KUMAR RAY
Keyword(s):  
Reverse Engineering ◽  
Linear Time ◽  
Line Drawing ◽  
Approximation Error ◽  
Zero Approximation ◽  
Polygonal Approximation ◽  
Digital Curve ◽  
Symmetric Approximation ◽  
Engineering Concept ◽  
Automatic Algorithm

This paper applies reverse engineering on the Bresenham's line drawing algorithm [J. E. Bresenham, IBM System Journal, 4, 106–111 (1965)] for polygonal approximation of digital curve. The proposed method has a number of features, namely, it is sequential and runs in linear time, produces symmetric approximation from symmetric digital curve, is an automatic algorithm and the approximating polygon has the least non-zero approximation error as compared to other algorithms.

Automatic Algorithm for Generating Complex Polyhedral Scaffold Structures for Tissue Engineering

2004 ◽  
Vol 10 (3-4) ◽  
pp. 595-610 ◽  
Author(s):  
Chi-Mun Cheah ◽  
Chee-Kai Chua ◽  
Kah-Fai Leong ◽  
Chee-How Cheong ◽  
May-Win Naing
Keyword(s):  
Tissue Engineering ◽  
Automatic Algorithm

scholarly journals AUTOMATIC ALGORITHM DEVELOPMENT USING NEW REINFORCEMENT PROGRAMMING TECHNIQUES

2012 ◽  
Vol 28 (2) ◽  
pp. 176-208
Author(s):  
Spencer White ◽  
Tony Martinez ◽  
George Rudolph
Keyword(s):  
Algorithm Development ◽  
Programming Techniques ◽  
Automatic Algorithm

IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol 184 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Gal Amit ◽  
Hanan Datz
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Method ◽  
Algorithm Performance ◽  
Artificial Neural ◽  
First Time ◽  
Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

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