Vol. 21 Núm. 3 (2022): Revista UIS Ingenierías
Artículos

Segmentación de imágenes mediante agrupación espectral y medida de similaridad difusa

Juan Pablo Rodríguez-Fernández
Universidad del Cauca
Pablo Lizarazo-Chilamá
Universidad del Cauca
Elena Muñoz-España
Universidad del Cauca
Juan Fernando Flórez-Marulanda
Universidad del Cauca

Publicado 2022-06-22

Palabras clave

  • agrupamiento espectral,
  • medida de similitud difusa,
  • segmentación de imágenes,
  • superpíxeles,
  • carga computacional,
  • disminución de costo computacional
  • ...Más
    Menos

Cómo citar

Rodríguez-Fernández, J. P., Lizarazo-Chilamá, P., Muñoz-España, E., & Flórez-Marulanda, J. F. (2022). Segmentación de imágenes mediante agrupación espectral y medida de similaridad difusa. Revista UIS Ingenierías, 21(3), 9–20. https://doi.org/10.18273/revuin.v21n3-2022002

Resumen

En los algoritmos de segmentación de imágenes mediante agrupamiento espectral, debido al tamaño de las imágenes, la carga computacional para la construcción de la matriz de similitud y la solución al problema de valores propios para la matriz laplaciana son altos. Además, la medida de similitud más utilizada es el kernel gaussiano, el cual presenta problemas con distribuciones de datos irregulares. Este trabajo propone realizar una presegmentación o diezmado mediante superpíxeles con el algoritmo Simple Linear Iterative Clustering, para disminuir el costo computacional y construir la matriz de similaridad con una medida difusa basada en el clasificador Fuzzy C-Means, que proporciona al algoritmo una mayor robustez frente a imágenes con distribuciones complejas; mediante agrupamiento espectral se determina la segmentación final. Experimentalmente, se comprobó que el enfoque propuesto obtiene segmentaciones adecuadas, buenos resultados de agrupamiento y una precisión comparable respecto a cinco algoritmos, midiendo el desempeño bajo cuatro métricas de validación.

Descargas

Los datos de descargas todavía no están disponibles.

Referencias

  1. D. Kaur, Y. Kaur, “Various Image Segmentation Techniques: A Review”, Int. J. Comput. Sci. Mob. Comput., vol. 3, no. 5, pp. 809-814, 2014.
  2. N. R. Pal, S. K. Pal, “A review on image segmentation techniques”, Pattern Recognit., vol. 26, no. 9, pp. 1277-1294, 1993, doi: https://doi.org/10.1016/0031-3203(93)90135-J
  3. F. Sultana, A. Sufian, P. Dutta, “Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey”, Knowledge-Based Systems, vol 201-202, 2020, doi: https://doi.org/10.1016/j.knosys.2020.106062
  4. C.L. Chowdhary, D.P. Acharjya, “Segmentation and Feature Extraction in Medical Imaging: A Systematic Review”, Procedia Computer Science, vol 167, pp 26-36, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.179
  5. S. Zeng, R. Huang, Z. Kang, N. Sang, “Image segmentation using spectral clustering of Gaussian mixture models”, Neurocomputing, vol. 144, pp. 346-356, Nov, 2014. doi: https://doi.org/10.1016/j.neucom.2014.04.037
  6. X. Wang, Z. Yang, X. Yue, H. Wang, “A Group Norm Regularized Factorization Model for Subspace Segmentation”, IEEE Transactions on Cybernetics, vol 8, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3000816
  7. M. Angulakshmi, G.G. Lakshmi Priya, “Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation”, IRBM, vol 40, no. 5, pp. 253-262, 2019, doi: https://doi.org/10.1016/j.irbm.2019.04.005
  8. M. Angulakshmi, G.G. Lakshmi Priya. “Brain tumour segmentation from MRI using superpixels based spectral clustering”, Journal of King Saud University - Computer and Information Sciences, 2018, doi: https://doi.org/10.1016/j.jksuci.2018.01.009
  9. N. He, X. Zhang, J. Zhao, et al. “Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity”, Int. Conf. Digital Image Process., Hong Kong, 2017, doi: https://doi.org/10.1117/12.2281942
  10. M. Spindler, C. M. Thiel, “Quantitative magnetic resonance imaging for segmentation and white matter extraction of the hypothalamus,” Journal of Neuroscience Research, vol. 100, no. 2, pp. 564-577, 2022, doi: https://doi.org/10.1002/jnr.24988
  11. J. Hou, W. Liu, X. E, H. Cui, “Towards parameter-independent data clustering and image segmentation”, Pattern Recognit., vol. 60, pp. 25-36, 2016, doi: https://doi.org/10.1016/j.patcog.2016.04.015
  12. N. Qiao, L. Di, “An improved method of linear spectral clustering”, Multimedia Tools and Applications 2021, vol. 81, no. 1, pp. 1287-1311, 2021, doi: https://doi.org/10.1007/s11042-021-11459-x
  13. X. L. Jiang, Q. Wang, B. He, S. J. Chen, B. L. Li, “Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints”, Neurocomputing, vol. 207, pp. 22-35, 2016, doi: https://doi.org/10.1016/j.neucom.2016.03.046
  14. T. Inkaya, “A parameter-free similarity graph for spectral clustering”, Expert Syst. Appl., vol. 42, no. 24, pp. 9489-9498, Dec. 2015, doi: https://doi.org/10.1016/j.eswa.2015.07.074
  15. A. Mur, R. Dormido, N. Duro, S. Dormido, J. Vega. “Determination of the optimal number of clusters using a spectral clustering optimization”, Expert Systems with Applications, vol 65, pp 304-314, 2016, doi: https://doi.org/10.1016/j.eswa.2016.08.059
  16. F. Zhao, H. Liu, L. Jiao, “Spectral clustering with fuzzy similarity measure”, Digit. Signal Processing, vol. 21, no. 6, pp. 701-709, 2011, doi: https://doi.org/10.1016/j.dsp.2011.07.002
  17. Y. Yang, Y. Wang, “Simulated annealing spectral clustering algorithm for image segmentation”, J. Syst. Eng. Electron., vol. 25, no. 3, pp. 514-522, 2014, doi: https://doi.org/10.1109/JSEE.2014.00059
  18. H. Chang, D.Y Yeung, “Robust path-based spectral clustering with application to image segmentation”, Tenth IEEE Int. Conf. Comput. Vis, vol. 1, Beijing, 2005, pp 278-285, doi: https://doi.org/10.1109/ICCV.2005.210
  19. X. Fan, L. Ju, X. Wang, S. Wang, “A fuzzy edge-weighted centroidal Voronoi tessellation model for image segmentation”, Computers and Mathematics with Applications, vol. 71, no. 11, pp. 2272-2284, 2016. doi: https://doi.org/10.1016/j.camwa.2015.11.003
  20. L. Fang, X. Wang, Z. Lian, Y. Yao, and Y. Zhang, “Supervoxel-based brain tumor segmentation with multimodal MRI images,” Signal, Image and Video Processing, vol. 16, no. 5, pp. 1215-1223, 2022, doi: https://doi.org/10.1007/s11760-021-02072-4
  21. E. Zimudzi, I. Sanders, N. Rollings, C. Omlin, “Segmenting mangrove ecosystems drone images using SLIC superpixels,” Geocarto International, vol. 34, no. 14, pp. 1648-1662, 2018, doi: https://doi.org/10.1080/10106049.2018.1497093
  22. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, “SLIC Superpixels”, EPFL Tech. Rep. 149300, June, p. 15, 2010.
  23. X. D. Bai, Z. G. Cao, Y. Wang, M. N. Ye, L. Zhu, “Image segmentation using modified SLIC and Nyström based spectral clustering”, Optik, vol. 125, no. 16, pp. 4302-4307, 2014, doi: https://doi.org/10.1016/j.ijleo.2014.03.035
  24. Z. Li, J. Chen, “Superpixel segmentation using Linear Spectral Clustering”, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 1356-1363, doi: https://doi.org/10.1109/CVPR.2015.7298741
  25. Y. Yang, Y. Wang, X. Xue, “A novel spectral clustering method with superpixels for image segmentation”, Optik, vol. 127, no. 1, pp. 161-167, 2016, doi: https://doi.org/10.1016/j.ijleo.2015.10.053
  26. A. Schick, M. Fischer, R. Stiefelhagen, “An evaluation of the compactness of superpixels”, Pattern Recognit. Lett., vol. 43, no. 1, pp. 71-80, 2014, doi: https://doi.org/10.1016/j.patrec.2013.09.013
  27. K. B. Schloss, L. Lessard, C. Racey, A. C. Hurlbert, “Modeling color preference using color space metrics”, Vision Research, vol 151, pp. 99-116, 2018, doi: https://doi.org/10.1016/j.visres.2017.07.001
  28. S. E. Schaeffer, “Graph clustering”, Computer Science Review, vol. 1, no. 1, pp. 27-64, 2007, doi: https://doi.org/10.1016/j.cosrev.2007.05.001
  29. D. Martin, C. Fowlkes, D. Tal, J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics”, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, Vancouver, BC, Canada, 2001, pp. 416-423, doi: https://doi.org/10.1109/ICCV.2001.937655
  30. J. Shi, J. Malik, “Normalized cuts and image segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000. doi: https://doi.org/10.1109/34.868688
  31. C. Fowlkes, S. Belongie, J. Malik, “Efficient spatiotemporal grouping using the Nyström method”, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Kauai, HI, USA, 2001, pp. I-I, doi: https://doi.org/10.1109/CVPR.2001.990481
  32. Y. Ng. Andrew, M. I Jordan, Y. Weiss, “On Spectral Clustering: Analysis and an algorithm”, Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic. January 2001, pp. 849-856.
  33. N. Seo, “Normalized Cuts and Image Segmentation,” 2006 [En línea]. Disponible en:http://note.sonots.com/SciSoftware/NcutImageSegmentation.html.
  34. X. D. Bai, Z. G. Cao, Y. Wang, M. N. Ye, L. Zhu, “Image segmentation using modified SLIC and Nyström based spectral clustering”, Optik (Stuttg)., vol. 125, no. 16, pp. 4302-4307, 2014, doi: https://doi.org/10.1016/j.ijleo.2014.03.035
  35. H. Zhang, J. E. Fritts, S. A. Goldman, “Image segmentation evaluation: A survey of unsupervised methods”, Comput. Vis. Image Underst., vol. 110, no. 2, pp. 260-280, 2008, doi: https://doi.org/10.1016/j.cviu.2007.08.003
  36. T. Fawcett, “ROC Graphs: Notes and Practical Considerations for Data Mining Researchers ROC Graphs: Notes and Practical Considerations for Data Mining Researchers”, ReCALL, p. 27, 2003.
  37. R. Iakymchuk, “Performance prediction through time measurements”, International Conference on High Performance Computing, Kyiv, Ukraine, October 12-14, 2011.
  38. R. Guerequeta, A. Vallecillo, Técnicas de diseño de algoritmos. Servicio de Publicaciones e Intercambio Científico de la Universidad de Málaga, 2000.