Artery Segmentation in Ultrasound Images Based on an Evolutionary Scheme
Abstract
:1. Introduction
2. Materials and Methods
2.1. Evaluated Segmentation Methods
2.1.1. Parametric Active Contour
2.1.2. Region-Based Active Contour Model (ACM)
2.1.3. Segmentation Based on Fuzzy C-Mean Clustering
2.1.4. Active Shape Models (ASMs)
2.2. Proposed Segmentation Method
y(t) = yc + a ∙ cost ∙ sinθ − b ∙ sint ∙ cosθ
- 1 Population = InitPopulation(MaxPar,MinPar);
- 2 FitPop = GetFitness(Population);
- 3 BestAgent = GetBestAgent(Population);
- 4 while (NumIter < NumIterMax)
- 5 MutPop = Mutate(Population,BestAgent,F);
- 6 CrPop = Cross(Population,MutPop,CR);
- 7 FitCr = GetFitness(CrPop);
- 8 Population = Replace(Population,CRPop,FitCr,FitPop);
- 9 BestAgent = GetBestAgent(Population);
- 10 NumIter = NumIter + 1;
- 11 end while
- 12 return BestAgent
2.2.1. Feature Extraction
2.2.2. Ellipse Parameter Estimation
2.3. GPU Implementation
Method | GPU Time | CPU (8 cores) Time |
---|---|---|
SRAD (AOS)5 Iterations | 7.65 ms | 13.58 ms |
FRS | 3.78 ms | - |
Pixel Orientation | 1.14 ms | 6.95 ms |
Non-Max Suppression | 0.16 ms | 2.29 ms |
LIP-Sobel Gradient | 0.11 ms | 1.42 ms |
3. Results
- (1)
- DE/Best/1
- (2)
- DE/Current to Best/1
- (3)
- DE/Current to Best/2
- (4)
- DE/Rand/1
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Yao, J.; Kharma, N.; Grogono, P. A multi-population genetic algorithm for robust and fast ellipse detection. Pattern Anal. Appl. 2005, 8, 149–162. [Google Scholar] [CrossRef]
- McLaughlin, R.A. Randomized Hough transform: Improved ellipse detection with comparison. Pattern Recogn. Lett. 1998, 19, 299–305. [Google Scholar] [CrossRef]
- Lutton, E.; Martinez, P. A Genetic Algorithm for the Detection of 2D Geometric Primitives in Images. In Proceedings of the 12th International Conference on Pattern Recognition, Jerusalem, Israel, 9–13 October 1994; pp. 526–528.
- Mainzer, T. Genetic Algorithm for Shape Detection. Technical Report No. DCSE/TR-2002-06. University of West Bohemia: Pilsen, Czech Republic, 2002. [Google Scholar]
- Moursi, S.G.; Sakka, M.R.E. Semi-automatic snake based segmentation of carotid artery ultrasound images. Commun. Arab Comput. Soc. (ACS) 2009, 2, 1–32. [Google Scholar]
- Kass, M.; Witkin, A.; Terzopoulos, D. Snakes: Active contour models. Int. J. Comput. Vis. 1987, 1, 321–331. [Google Scholar]
- Xu, C.; Prince, J.L. Snakes, shapes, and gradient vector flow. IEEE T. Image Process. 1998, 7, 359–369. [Google Scholar] [CrossRef]
- Cohen, L.D.; Cohen, I. Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE T. Pattern Anal. 1993, 15, 1131–1147. [Google Scholar] [CrossRef]
- Ciecholewski, M. Gallbladder Boundary Segmentation from Ultrasound Images Using Active Contour Model. In Intelligent Data Engineering and Automated Learning–IDEAL; Springer: Berlin/Heidelberg, Germany, 2010; pp. 63–69. [Google Scholar]
- Cvancarova, M.; Albregtsen, F.; Brabrand, K.; Samset, E. Segmentation of Ultrasound Images of Liver Tumors Applying Snake Algorithms and GVF. In Proceedings of the 19th International Congress and Exhibition: Computer Assisted Radiology and Surgery (CARS 2005), Berlin, Germany, 22–25 June 2005; pp. 218–223.
- Tang, J. A multi-direction GVF snake for the segmentation of skin cancer images. Pattern Recogn. 2009, 42, 1172–1179. [Google Scholar] [CrossRef]
- Zhang, K.; Zhang, L.; Song, H.; Zhou, W. Active contours with selective local or global segmentation: A new formulation and level set method. Image Vis. Comput. 2010, 28, 668–676. [Google Scholar] [CrossRef]
- Caselles, V.; Kimmel, R.; Sapiro, G. Geodesic active contours. Int. J. Comput. Vis. 1997, 22, 61–79. [Google Scholar] [CrossRef]
- Chan, T.F.; Vese, L.A. Active contours without edges. IEEE T. Image Process. 2001, 10, 266–277. [Google Scholar] [CrossRef]
- Dunn, J.C. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybernet. 1973, 3, 32–57. [Google Scholar] [CrossRef]
- Abdel-Dayem, A.; El-Sakka, M. Fuzzy c-Means Clustering for Segmenting Carotid Artery Ultrasound Images. In Proceedings of the International Conference on Image Analysis and Recognition (ICIAR 2007), Montreal, Canada, 22–24 August 2007; pp. 933–948.
- Vincent, L. Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE T. Image Process. 1993, 2, 176–201. [Google Scholar] [CrossRef]
- Cootes, T.F.; Taylor, C.J.; Cooper, D.H.; Graham, J. Active shape models—Their training and application. Comput. Vis. Image Underst. 1995, 61, 38–59. [Google Scholar] [CrossRef]
- Storn, R.; Price, K. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 1997, 11, 341–359. [Google Scholar] [CrossRef]
- Hansen, N. Compilation of results on the 2005 CEC benchmark function set. Tech. Rep. Institute of Computational Science ETH Zurich. 2006. [Google Scholar]
- Loy, G.; Zelinsky, A. Fast radial symmetry for detecting points of interest. IEEE T. Pattern Anal. 2003, 25, 959–973. [Google Scholar] [CrossRef]
- Yongjian, Y.; Acton, S.T. Speckle reducing anisotropic diffusion. IEEE T. Image Process. 2002, 11, 1260–1270. [Google Scholar] [CrossRef]
- Palomares, J.M.; Gonzalez, J.; Ros, E.; Prieto, A. General logarithmic image processing. IEEE T. Image Process. 2006, 15, 3602–3608. [Google Scholar] [CrossRef]
- Deng, G.; Cahill, L.W.; Tobin, G.R. The study of logarithmic image processing model and its application to image enhancement. IEEE T. Image Process. 1995, 506–512. [Google Scholar] [CrossRef]
- Canny, J.F. A computational approach to edge detection. IEEE T. Pattern Anal. 1986, 8, 679–698. [Google Scholar] [CrossRef]
- Hajela, P.; Lin, C.Y. Genetic search strategies in multicriterion optimal design. Struct. Optim. 1992, 4, 99–107. [Google Scholar] [CrossRef]
- De Veronses, L.; Krohling, R. Differential Evolution Algorithm on the GPU with C-CUDA. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain, 18–23 July 2010; pp. 1–7.
- Zhu, W. Massively parallel differential evolution-pattern search optimization with graphics hardware acceleration: An investigation on bound constrained optimization problems. J. Glob. Optim. 2011, 50, 417–437. [Google Scholar] [CrossRef]
- Zhu, W.; Li, Y. GPU-Accelerated Differential Evolutionary Markov Chain Monte Carlo Method for Multi-Objective Optimization over Continuous Space. In Proceedings of the 2nd Workshop on Bio-Inspired Algorithms for Distributed Systems (BADS), New York, NY, USA, 7–11 June 2010; pp. 1–8.
- Marsaglia, G. Xorshift RNGs. J. Stat. Softw. 2003, 8, 1–6. [Google Scholar]
- Mallipeddi, R.; Suganthan, P.N. Empirical Study on the Effect of Population Size on Differential Evolution Algorithm. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Hong Kong, China, 1–6 June 2008; pp. 3664–3671.
- Weichert, J. Anisotropic Diffusion in Image Processing; BG Teubner: Stuttgart, Germany, 2008; pp. 278–290. [Google Scholar]
- Weickert, J.; Romeny, B.H.; Viergever, M.A. Efficient and reliable schemes for nonlinear diffusion filtering. IEEE T. Image Process. 1998, 7, 398–409. [Google Scholar] [CrossRef]
- Cao, T.; Wang, B.; Liu, D.C. Optimized GPU Framework for Semi-Implicit AOS Scheme Based Speckle Reducing Nolinear Diffusion. In Proceedings of the SPIE Medical Imaging; Lake Buena Vista, FL, USA: 8–12 February 2009.
- Hockney, R.W. A fast direct solution of Poisson’s equation using Fourier analysis. J. ACM 1965, 12, 95–113. [Google Scholar] [CrossRef]
- Hockney, R.W.; Jesshope, C.R. Parallel Computers; Adam Hilger: London, UK, 1981. [Google Scholar]
- Zhang, Y.; Cohen, J.; Owens, J.D. Fast Tridiagonal Solvers on the GPU. In Proceedings of the 15th ACM SIGPLAN Symposium Principles and Practice of Parallel Programming (PPoPP ’10), Bangalore, India, 9–14 January 2010; pp. 127–136.
- Glavtchev, V.; Muyan-Ozcelik, P.; Ota, J.M.; Owens, J.D. Feature-Based Speed Limit Sign Detection Using a Graphics Processing Unit. In Proceedings of the IEEE on Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, 5–9 June 2011; pp. 195–200.
- Palomar, R.; Palomares, J.M.; Castillo, J.M.; Olivares, J.; Gómez-Luna, J. Parallelizing and Optimizing LIP-Canny Using Nvidia Cuda. In Trends in Applied Intelligent Systems; Springer: Berlin/Heidelberg, Germany, 2010; pp. 389–398. [Google Scholar]
© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
Share and Cite
Guzman, P.; Ros, R.; Ros, E. Artery Segmentation in Ultrasound Images Based on an Evolutionary Scheme. Informatics 2014, 1, 52-71. https://doi.org/10.3390/informatics1010052
Guzman P, Ros R, Ros E. Artery Segmentation in Ultrasound Images Based on an Evolutionary Scheme. Informatics. 2014; 1(1):52-71. https://doi.org/10.3390/informatics1010052
Chicago/Turabian StyleGuzman, Pablo, Rafael Ros, and Eduardo Ros. 2014. "Artery Segmentation in Ultrasound Images Based on an Evolutionary Scheme" Informatics 1, no. 1: 52-71. https://doi.org/10.3390/informatics1010052