Brain cine MRI segmentation based on a multiagent algorithm for dynamic continuous optimization

In this paper, we propose a multiagent based evolution strategy algorithm, called CMADO, to evaluate the amplitudes of the deformations of the walls of the third cerebral ventricle on a brain cine-MR imaging. CMADO based segmentation technique is applied on a 2D+t dataset to detect the contours of the region of interest (i.e. lamina terminalis). Then, the successive segmented contours are matched using a procedure of global alignment. Finally, local measurements of deformations are derived from the previously determined matched contours. The validation step is realized by comparing our results to the measurements achieved on the same patients through a manual segmentation provided by an expert using Ethovision® software.

[1]  Jürgen Branke *,et al.  Anticipation and flexibility in dynamic scheduling , 2005 .

[2]  P. Siarry,et al.  Non-supervised image segmentation based on multiobjective optimization , 2008, Pattern Recognit. Lett..

[3]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[4]  Amir Nakib,et al.  Image histogram thresholding based on multiobjective optimization , 2007, Signal Process..

[5]  Antonio Barrientos,et al.  Two adaptive mutation operators for optima tracking in dynamic optimization problems with evolution strategies , 2007, GECCO '07.

[6]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[7]  Dumitru Dumitrescu,et al.  ESCA: A New Evolutionary-Swarm Cooperative Algorithm , 2007, NICSO.

[8]  Dumitru Dumitrescu,et al.  Collaborative Evolutionary Swarm Optimization with a Gauss Chaotic Sequence Generator , 2008, Innovations in Hybrid Intelligent Systems.

[9]  Tim Hendtlass,et al.  A simple and efficient multi-component algorithm for solving dynamic function optimisation problems , 2007, 2007 IEEE Congress on Evolutionary Computation.

[10]  Amir Nakib,et al.  A New Multiagent Algorithm for Dynamic Continuous Optimization , 2010, Int. J. Appl. Metaheuristic Comput..

[11]  Claudio Rossi,et al.  Tracking Moving Optima Using Kalman-Based Predictions , 2008, Evolutionary Computation.

[12]  Anabela Simões,et al.  Evolutionary Algorithms for Dynamic Environments: Prediction Using Linear Regression and Markov Chains , 2008, PPSN.

[13]  Jérôme Hodel,et al.  Brain ventricular wall movement assessed by a gated cine MR trueFISP sequence in patients treated with endoscopic third ventriculostomy , 2009, European Radiology.

[14]  W. Fischer,et al.  Sphere Packings, Lattices and Groups , 1990 .

[15]  Azriel Rosenfeld,et al.  Histogram modification for threshold selection , 1977 .