Hidden Markov Random Fields and Swarm Particles: a Winning Combination in Image Segmentation
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The document presents the HMRF-PSO method for image segmentation, combining Hidden Markov Random Fields with Particle Swarm Optimization for improved results. The method is evaluated on non-destructive testing datasets, showing it outperforms traditional thresholding methods in terms of misclassification error. While demonstrating robustness and noise resistance, the challenges of parameter selection remain noted.
Hidden Markov Random Fields and Swarm Particles: a Winning Combination in Image Segmentation
- 1. Hidden Markov Random Fields and Swarm Particles: a Winning Combination in Image Segmentation 2014 International Conference on Future Information Engineering EL-Hachemi Guerrout, Teacher at ESI Samy Ait-Aoudia, Professor at ESI Ramdane Mahiou, Teacher at ESI
- 2. The goal of segmentation: To simplify the representation of an image The image became more meaningful and easier to analyze Introduction - Why the segmentation ? 2
- 3. Medical imaging Locate tumors Diagnosis pathologies … Object detection Pedestrian detection Face detection Locate objects in satellite images (roads, forests, crops, etc.) …. Recognition Tasks Face recognition Fingerprint recognition Iris recognition ….. Video surveillance Compression (Video, image) Traffic control systems ….. Application Domains 3
- 4. 1 2 3 4 2C,s 2 s ),(2-(1)2ln( 2 )²-(y y)(x, t tsx Ss x x xx Ts s s y)(x,minarg Xx x Y: Observed Image X: Hidden Image HMRF provides an elegant way to model the segmentation problem This elegant model leads to an optimization problem Our new proposed approach based on PSO We looking for The Hidden Image where : Hidden Markov Random Filed 4
- 5. Particles Swarm Optimization Particle related to bird flocking or fish schooling what's the strategy to find the food? A group of Particles are randomly searching food in an area Each particle has a velocity and position The next position of each particle is influenced by: The best position, visited by itself The best position, visited by all particles 5
- 6. Particles Swarm Optimization Advantages Very efficient global search algorithm Simple implementation Easily parallelized for concurrent processing Disadvantages How to choose parameters ? 6
- 7. HMRF-PSO method Experimental Results-(1) Our tests based on Non Destructive Testing (NDT) DataSet Examples of NDT application: Ultrasonic inspection of defective aircraft materials such as carbon fiber reinforced (CFRP) composites Thermal inspection of glass-fiber reinforced (GFRP) Eddy current inspection of aircraft wheel fuselage cracks Inspection of coating depth of steel plates Observation of surface roughness of metals and ceramics Defects in printed circuit board images In textile image 7
- 8. (a) (b) (c) (d) (e) (f) Here some NDT images to segment used in our tests HMRF-PSO method Experimental Results-(2) 8
- 9. (a) (b) (c) (d) (e) (f) Ground truth images (original images) of the images listed before (a) (b) (c) (d) (e) (f) Here we list the segmented images (tested images) using HMRF-PSO for the parameters: size=80, c1=0.7, c2=0.8, w=0.7, vmax=5, iteration_number=100 and B=1 HMRF-PSO method Experimental Results-(3) 9
- 10. Misclassification Error (ME) ME is used to evaluate the quality of segmentation ME =0 The best case ME=1 The worst case F FF 1ME BO and FO denote the background and foreground of the original (ground-truth) image BT and FT denote background and foreground of the segmented image HMRF-PSO method Experimental Results-(4) 10
- 11. HMRF-PSO method Experimental Results-(5) Method Image (a) Image (b) Image (c) Image (d) Image (e) Image (f) Abutaleb 0.023 0.310 0.023 0.024 0.250 0.620 Kittler-Ill. 0.000 0.003 0.037 0.008 0.025 0.028 Kapur et al. 0.003 0.004 0.028 0.036 0.220 0.620 Tsai 0.240 0.170 0.350 0.290 0.084 0.280 Li & Lee 0.490 0.550 0.450 0.710 0.021 0.020 Pham 0.460 0.560 0.021 0.760 0.048 0.250 SemiV 0.003 0.004 0.026 0.018 0.062 0.160 Otsu 0.462 0.513 0.413 0.021 0.037 0.074 Median extension 0.462 0.527 0.474 0.608 0.028 0.039 MoG 0.000 0.000 0.032 0.010 0.018 0.012 MoGG 0.000 0.000 0.028 0.007 0.012 0.016 HMRF-PSO 0.000 0.000 0.018 0.001 0.004 0.005 Misclassification errors in NDT segmented images means the minimal error 11 11
- 12. Conclusion we have presented HMRF-PSO method and compared it to thresholding methods Performance evaluation was conducted on NDT image dataset Misclassification Error criterion was used as a performance metric From the results obtained, the HMRF-PSO combination method outperforms thresholding methods HMRF-PSO method demonstrates its robustness and resistance to noise Selecting parameters still not obvious task 12
- 13. Thank you for your
Editor's Notes
- #2: Hello everybody, I will present my work titled : Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach
- #5: HMRF is a strong model for image segmentation is to see the image to segment as a realization of a Markov Random Field Y={Ys}sS. And The segmented image is seen as the realization of another Markov Random Field X , can be found it by maximizing the function (x,y).