Abstract
Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. This paper presents a parallel approach for fast and robust object detection in a medical image. First, the proposed approach consists to decompose the image into multiple resolutions by a Gaussian pyramid algorithm. Then, the object detection in the higher pyramids levels is done in parallel by a Hybrid model combining Watershed algorithm, GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models where the initial contour is subdivided into sub-contours, which are independent from each other. Each sub-contour converges independently in parallel. The last step of our approach consists to project the sub-contours detected in the low resolution image to the high-resolution image. The experimental results were performed using a number of synthetic and medical images. Its rapidity is justified by runtime comparison with a conventional method.
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References
kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 55, 321–331 (1988)
Ashraf, A., Safaai, B., Nazar, Z.: A novel image segmentation enhancement technique based on active contour and topological alignments. Adv. Comput. Int. J. (ACIJ) 2(3), 1–7 (2011)
Inderpreet, K., Amandeep, K.: Modified active contour snake model for image segmentation using anisotropic filtering. IRJET 3(5) (2016)
Jaiswal, R.S., Sarode, M.V.: A review on role of active contour model in image segmentation applications. IJARCCE 6(5) (2017)
Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)
Xu, C., Prince, J.: Generalized gradient vector flow external forces for active contours. Signal Process. 71, 131–139 (1998)
Ning, J., Wu, C., Liu, S., Yang, S.: NGVF: an improved external force field for active contour model. Pattern Recognit. Lett 28, 58–93 (2007)
Wang, Y., Liu, L., Zhang, H., Cao, Z., Lu, S.: Image segmentation using active contours with normally biased GVF external force. IEEE Signal Process. Lett. 17, 875–878 (2010)
Zhang, R., Shiping, Z., Zhou, Q.: A novel gradient vector flow snake model based on convex function for infrared image segmentation. 16, 1756 (2016). https://doi.org/10.3390/s16101756
Mengmeng, Z., Qianqian Li., Lei Li., Peirui, B.: An improved algorithm based on the GVF-snake for effective concavity edge detection. J. Softw. Eng. Appl. 6, 174–178 (2013)
Jayadevappa, D., Srinivas Kumar, S., Murty, D.: A hybrid segmentation model based on watershed and gradient vector flow for the detection of brain tumor. Int. J. Signal Process. Image Process. Pattern Recognit. 2(3), 29–42 (2009)
Curwen, R.M., Blake, A., Cipolla, R.: Parallel implementation of Lagrangian dynamics for real-time snakes. In: Proceedings of the British Machine Vision Conference, pp. 29–35 (1991)
Wakatani, A.: A scalable parallel algorithm for the extraction of active contour. In: Proceedings of the PARELEC, Trois- Rivieres, Quebec, Canada, 27–30 August, pp. 94–98 (2000)
Rossantl, F., et al.: Parallel double snakes. Application to the segmentation of retinal layers in 2D-OCT for pathological subjects. J. Med. Imaging Health Inform. 48, 3857 (2015)
Fekir, A., Benamrane, N.: Segmentation of medical image sequence by parallel active contour. Adv. Exp. Med. Biol. 696, 515–522 (2011)
Mostafa, M.G., Tolba, M.F., Gharib, F.F., A-Megeed, M.: A Gaussian multiresolution algorithm for medical image segmentation. In: IEEE 7th International Conference on Intelligent Engineering Systems, INES, Assiut-Luxor, Egypt (2003)
Kim, J.B., Kim, H.J.: Multiresolution based watersheds for efficient image segmentation. Pattern Recognit. Lett. 24(1–3), 473–488 (2003)
Zhang, T., Mu, D.-j., Ren, S.: Information hiding (IH) algorithm based on gaussian pyramid and ghm multi-wavelet transformation. Int. J. Digit. Content Technol. Its Appl. 5(3), 210 (2011)
Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transform. In: Dougherty, E.R. (ed.) Mathematical Morphology in Image Processing, vol. 12, pp. 433–481. Marcel Dekker, New York (1993)
Jos, B.T.M, R., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41, 187–228 (2001)
Lam, K., Yan, H.: Fast greedy algorithm for active contours. Electron. Lett. 30(1), 21–23 (1994)
Williams, D., Sham, M.: A fast algorithm for active contour and curvature estimation. Comput. Vis. Graph. Image Process. Image Underst. 55(1), 14–26 (1992)
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Meddeber, H., Yagoubi, B. (2019). A New Parallel Method for Medical Image Segmentation Using Watershed Algorithm and an Improved Gradient Vector Flow. In: Rocha, Á., Serrhini, M. (eds) Information Systems and Technologies to Support Learning. EMENA-ISTL 2018. Smart Innovation, Systems and Technologies, vol 111. Springer, Cham. https://doi.org/10.1007/978-3-030-03577-8_70
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DOI: https://doi.org/10.1007/978-3-030-03577-8_70
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