Real time implementation of medical images segmentation based on PSO

Segmentation is an important technique that is applied to prepare image for detection, classification and recognition steps. To successfully achieve these steps, we have to successfully realize the segmentation step. Therefore, several approaches have been developed yet. One of these algorithms is based on the technique of Particle Swarm Optimization (PSO). It has been widely applied in the literature. This paper deals with hardware implementation of PSO algorithm for medical images segmentation using Xilinx System Generator (XSG). The use of the visual development process models of Simulink facilitates the RTL level simulation and validation such as the synthesis of VHDL code. Also, all legacy codes written in Matlab can be used again in custom blocks. The performances of the proposed method are demonstrated using a set of medical images.

[1]  Abdellatif Mtibaa,et al.  Real Time Implementation of Detection of Bacteria in Microscopic Images Using System Generator , 2012 .

[2]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Bernd Jähne,et al.  Computer vision and applications: a guide for students and practitioners , 2000 .

[5]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[6]  Jean-Marc Constans,et al.  A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images , 2007, Image Vis. Comput..

[7]  Jean-Gabriel Mailloux Prototypage rapide de la commande vectorielle sur FPGA à l'aide des outils Simulink - System Generator / , 2008 .

[8]  Ana Toledo Moreo,et al.  Experiences on developing computer vision hardware algorithms using Xilinx system generator , 2005, Microprocess. Microsystems.

[9]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[10]  Dervis Karaboga,et al.  A novel clustering approach: Artificial Bee Colony (ABC) algorithm , 2011, Appl. Soft Comput..

[11]  Ujjwal Maulik,et al.  Genetic algorithm-based clustering technique , 2000, Pattern Recognit..

[12]  Patricia Melin,et al.  Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications , 2013, Appl. Soft Comput..

[13]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[14]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[15]  W ReynoldsCraig Flocks, herds and schools: A distributed behavioral model , 1987 .

[16]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[17]  Pengfei Shi,et al.  An improved ant colony algorithm for fuzzy clustering in image segmentation , 2007, Neurocomputing.