Selective Segmentation Model for Vector-Valued Images
DOI:
https://doi.org/10.32890/jict2022.21.2.1Keywords:
Active contour, colour image segmentation, selective segmentation, variational model, vector-valued imageAbstract
One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevertheless, if the input is vector-valued or identified as a colour image, the models simply ignore the colour information by converting that image into a greyscale format. Colour plays an important role in the interpretation of object boundaries within an image as it helps to provide a more detailed explanation of the scene’s objects. Therefore, in this research, a model for selective segmentation of vector-valued images is proposed by combining concepts from existing models. The finite difference method was used to solve the resulting Euler-Lagrange (EL) partial differential equation of the proposed model. The accuracy of the proposed model’s segmentation output was then assessed using visual observation as well as by using two similarity indices, namely the Jaccard (JSC) and Dice (DSC) similarity coefficients. Experimental results demonstrated that the proposed model is capable of successfully segmenting a specific object in vector-valued images. Future research on this area can be further extended in three-dimensional modelling.
References
Ali, H., Faisal, S., Chen, K., & Rada, L. (2020). Image-selective segmentation model for multi-regions within the object of interest with application to medical disease. Visual Computer, 37(5), 939-955. https://doi.org/10.1007/s0037 1-020-01845-1 Journal of ICT, 21, No. 2 (April) 2022, pp: 149-
Altarawneh, N. M., Luo, S., Regan, B., Sun, C., & Jia, F (2014). Global threshold and region-based active contour model for accurate image segmentation. Signal & Image Processing, 5(3), 1-11. https://doi.org/http://dx.doi.org/10.5121/sipij.2014.5301
Badshah, N., & Chen, K. (2010). Image selective segmentation under geometrical constraints using an active contour approach. Communications in Computational Physics, 7(4), 759-778. https://doi.org/http://dx.doi.org/10.4208/cicp.2009.09.026
Bala, A., & Sharma, A. K. (2017). Split and merge: A region based image segmentation. International Journal of Emerging Research in Management and Technology, 6(8), 306-309. https://doi.org/http://dx.doi.org/10.23956/ijermt.v6i8.
Chan, T. F., & Vese, L. A. (2001). Active contours without edges. JEEE Transactions on Image Processing, 10(2), 266-277. https://doi.org/http://dx.doi.org/10.1109/83.902291
Chan, T. F., Yezrielev Sandberg, B., & Vese, L. A. (2000). Active contours without edges for vector-valued images. Journal of Visual Communication and Image Representation, 11(2), 130-141. https://doi.org/http://dx.doi.org/10.1006/jvci.1999.0442 Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler,
H., & Halpern, A. (2018, April). Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In Proceedings - International Symposium on Biomedical Imaging, 2018-April (USBI) (pp. 168-172). https://doi.org/10.1109/
ISBI.2018.8363547
Dobrosotskaya, J., & Guo, W. (2017). A PDE-free variational method for multi-phase image segmentation based on multiscale sparse representations. Journal of Imaging, 3(3), 1-26. https://doi.org/10.3390/jimaging3030026 Embong, R., Aziz, N. M. N. A., Karim, A. H. A., & Ibrahim, M.
R. (2017). Colour application on mammography image segmentation. Colour Application on Mammography Image Segmentation, §890(1), 1-8. _ https://doi.org/http://dx.doi. org/10.1088/1742-6596/890/1/012066
Fang, J., Liu, H., Zhang, L., Liu, J., & Liu, H. (2021). Region-edge-based active contours driven by hybrid and local fuzzy region-based energy for image segmentation. Information Sciences, 546, 397-419. https://doi.org/10.1016/j.ins.2020.08.078 Journal of ICT, 21, No. 2 (April) 2022, pp: 149-
Fang, L., Qiu, T., Liu, Y., & Chen, C. (2018). Active contour model driven by global and local intensity information for ultrasound image segmentation. Computers & Mathematics with Applications, 75(12), 4286-4299. https://doi.org/10.1016/j. camwa.2018.03.029
Getreuer, P. (2012). Rudin—Osher—Fatemi total variation denoising using split Bregman. Image Processing on Line, 2(1), 74-95. https://doi.org/http://dx.doi.org/10.5201/ipol.2012.g-tvd Hore, S., Chakraborty, S., Chatterjee, S., Dey, N., Ashour, A. S.,
Van Chung, L., & Le, D. N. (2016). An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding. International Journal of Electrical and Computer Engineering, 6(6), 2773-2780. https://doi.org/http://dx.doi.org/10.1159 1/ijece.v616.pp2773-2780
Huang, Y., & Liu, Z. (2015). Segmentation and tracking of lymphocytes based on modified active contour models in phase contrast microscopy images. Computational and Mathematical Methods in Medicine, 2015. https://doi.org/http://dx.doi. org/10.1155/2015/693484
Jiang, X., Zhou, Z., Ding, X., Deng, X., Zou, L., & Li, B. (2017). Level set based hippocampus segmentation in MR images with improved initialization using region growing. Computational and Mathematical Methods in Medicine, 2017. https://doi.org/http://dx.doi.org/10.1155/2017/5256346
Jumaat, A. K., & Chen, K. (2020). Three-dimensional convex and selective variational image segmentation model. Malaysian Journal of Mathematical Sciences, 14(3), 437-450. https://einspem.upm.edu.my/journal/fullpaper/voll 4no03/7.%20 Abdul%20Kadir%20Jumaat.pdf
Jumaat, A. K., & Chen, K. (2019). A reformulated convex and selective variational image segmentation model and its fast multilevel algorithm. Numerical Mathematics Theory Methods and Applications, 12(2), 403-437. http://dx.doi.org/10.4208/ nmtma.OA-2017-0143
Jumaat, A. K., & Chen, K. (2017). An optimization-based multilevel algorithm for variational image segmentation models. Electronic Transactions on Numerical Analysis, 46, 474-504. https://etna. ricam.oeaw.ac.at/vol.46.2017/pp474-504.dir/pp474-504. pdf
Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321-331.https://www.researchgate.net/publication/284653608_ Snakes_Active_Contour_Models Journal of ICT, 21, No. 2 (April) 2022, pp: 149-
Li, H., Cai, J., Nguyen, T. N.A., & Zheng, J. (2013, July). A benchmark for semantic image segmentation. In JEEE International Conference on Multimedia and Expo (pp. 1-6.) https://doi.org/10.1109/ICME.2013.6607512
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001, July). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, (Vol. 2, pp. 416-423). https://doi.org/http://dx.doi.org/10.1109/ICCV.2001.937655
Mazouzi, S., & Guessoum, Z. (2021). A fast and fully distributed method for region-based image segmentation: Fast distributed region-based image segmentation. Journal of Real-Time Image Processing, 18(3), 793-806. https://doi.org/10.1007/s11554-020-01021-7 Moreira, I. C., Amaral, I, Domingues, I., Cardoso, A., Cardoso, M.
J., & Cardoso, J. S. (2012). INbreast: Toward a full-field digital mammographic database. Academic Radiology, 19(2), 236-248. https://doi.org/10.1016/j.acra.2011.09.01414
Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 42(5),577-685.https://doi.org/http://dx.doi.org/10.1002 cpa.3 160420503 Nguyen, T., Cai, J., Member, S., IEEE, Zhang, J., & Zheng, J. (2012). Robust interactive image segmentation using convex active contours. [EEE Transactions on Image Processing, 21(8), 3734-3743. https://doi.org/http://dx.doi.org/10.1109/
TIP.2012.2191566
Osher, Stanley, J., & Sethian, J. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Computational Physics, 79(1), 1-5. https://doi.org/http://dx.doi.org/10.1016/002 1-999 1%2888%2990002-2 Othman, M., Abdullah, S. L. S., Ahmad, K. A., Bakar, M. N. A., & Mansor, A. R. (2016). The fusion of edge detection and mathematical morphology algorithm for shape boundary recognition. Journal of Information and Communication Technology (JICT), 15(1), 133-144. http://e-journal.uum.edu. my/index.php/jict/article/view/8175 Journal of ICT, 21, No. 2 (April) 2022, pp: 149-
Rada, L., & Chen, K. (2011). A new variational model with dual level set functions for selective segmentation. Communications in Computational Physics, 12(1), 261-283. https://doi.org/https://doi.org/10.4208/CICP.190111.210611A
Rada, L., & Chen, K. (2013). Improved selective segmentation model using one level-set. Journal of Algorithms & Computational Technology, 7(4), 509-540. _ https://doi.org/http://dx.doi org/10.1260/1748-3018.7.4.509 Shaker, F., Monadjemi, S. A., Alirezaie, J., & Naghsh-Nilchi, A.
R. (2017). A dictionary learning approach for human sperm heads classification. Computers in Biology and Medicine, 91,181-190.https://doi.org/http://dx.doi.org/10.1016/j. compbiomed.2017.10.009
Shi, Y., Li, M., & Zeng, W. (2021). MARGM: A multi-subjects adaptive region growing method for group fMRI data analysis. Biomedical Signal Processing and Control, 69 (November 2020), 102882. https://doi.org/10.1016/j.bspc.2021.102882
Spencer, J., & Chen, K. E. (2015). A convex and selective variational model for image segmentation. Communications on Mathematics and Sciences, 13(6), 1453-1472. https://doi.org/http://dx.doi.org/10.4310/CMS.2015.v13.n6.a5
Vese, L. A., & Chan, T. F. (2002). A multiphase level set framework for image segmentation using the Mumford and Shah Model. International Journal of Computer Vision, 50(3), 271-293. https://doi.org/http://dx.doi.org/10.1023/A: 1020874308076
Wei, W. B., Tan, L., Jia, M. Q., & Pan, Z. K. (2017). Normal vector projection method used for convex optimization of Chan-Vese model for image segmentation. Journal of Physics: Conference Series, 787, Article 012016. https://iopscience.iop. org article/10.1088/1742-6596/787/1/012016
Xu, C., & Prince, J. L. (1998). Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7(3), 359-369. https://doi.org/http://dx.doi.org/10.1109/83.661186
Xue, Y., Zhao, J., & Zhang, M. (2021). A watershed-segmentation-based improved algorithm for extracting cultivated land boundaries. Remote Sensing, 13(5), 1-19. https://doi org/10.3390/ rs 13050939
Yearwood, A. B. (2018). A brief survey on variational methods for image segmentation (Research Assignment: Chicago Referencing). ResearchGate. https://www.researchgate.net/ publication/323971382_A_Brief_Survey_on_vVariational_ Methods_for_Image_Segmentation Journal of ICT, 21, No. 2 (April) 2022, pp: 149-
Yue, W. (2009). A simple introduction of active contour without edges. https://sites.google.com/s24ite/rexstribeofimageprocessing/ chan-vese-active-contours/wubiaotitiezi
Zhang, Y., Guo, H., Chen, F, & Yang, H. (2017). Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation. Neurocomputing, 249, 1-18. https://doi.org/http://dx.doi. org/10.1016/j.neucom.2017.01.044
Zhao, W., Xu, X., Zhu, Y., & Xu, F. (2018). Active contour model based on local and global Gaussian fitting energy for medical image segmentation. Optik, 158, 1160-1169. https://doi.org/10.1016/j.ijleo.2018.01.004
Published
Issue
Section
How to Cite
Research impact
Harvested 2026-09-05Counts differ between services because each indexes a different body of literature. None of them is the whole picture.
2002 - 2020
