Segmentation of soft tissues and tumors from biomedical images using optimized K-Means clustering via level set formulation

Автор: Ramudu Kama, Kalyani Chinegaram, Ranga Babu Tummala, Raghotham Reddy Ganta

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 9 vol.11, 2019 года.

Бесплатный доступ

Biomedical Image-segmentation is one of the ways towards removing an area of attentiveness by making various segments of an image. The segmentation of biomedical images is considered as one of the challenging tasks in many clinical applications due to poor illuminations, intensity inhomogeneity and noise. In this paper, we propose a new segmentation method which is called Optimized K-Means Clustering via Level Set Formulation. The proposed method diversified into two stages for efficient segmentation of soft tissues and tumor’s from MRI brain Scans Images, which is called pre-processing and post-processing. In the first stage, a hybrid approach is considered as pre-processing is called Optimized K-Means Clustering which is the combined approach of Particle Swarm Optimization (PSO) as well as K-Means Clustering for improve the clustering efficiency. We choose the ‘optimal’ cluster centers by Particle Swarm Optimization (PSO) algorithm for improving the clustering efficiency. During the process of pre-processing, these segmentation results suffer from few drawbacks such as outliers, edge and boundary leakage problems. In this regard, post-processing is necessary to minimize the obstacles, so we are implementing pre-processing results by using level-set method for smoothed and accurate segmentation of regions from biomedical images such as MRI brain images over existing level set methods.

Еще

Image segmentation, particle swarm optimization (PSO), K-Means Clustering Algorithm and level sets

Короткий адрес: https://readera.ru/15016620

IDR: 15016620   |   DOI: 10.5815/ijisa.2019.09.03

Список литературы Segmentation of soft tissues and tumors from biomedical images using optimized K-Means clustering via level set formulation

  • Bushberg, J.T.; Seibert, J.A.; Leidholdt, E.M.; Boone, J.M.: The Essential Physics of Medical Imaging 2nd Edn. Lippincott Williams & Wilkins, Baltimore ,2001
  • Gonzalez,R.C.;Woods,R.E.:DigitalImageProcessing,2ndEdn., Prentice Hall, New Jersey ,2002
  • Shapiro, L.G.; Stockman, G.C.: Computer Vision. Prentice Hall, New Jersey ,2001
  • Zhang .K, Zhang .L, Song .H, and Zhou .W, “Active contours with selective local or global segmentation: A new formulation and level set method,” Image Vis. Comput., Vol. 28, Apr. 2010, pp. 66876.
  • Bezdek, J.C.; Hall, L.O.; Clarke, L.P.: Review of MR image segmentation using pattern recognition. Med. Phys.20(4), 1993, pp.1033–1048
  • Chan .T, and Vese .L, “Active contours without edges,”IEEE Trans. Image Proc., Vol. 10, no. 2, Feb.2001, pp. 26677.
  • Dunn, J.C.: A fuzzy felative of the isodata process and its use in detecting compact well separated clusters. J. Cybernet. 3, 1973, pp. 26677
  • Lei, T.; Sew Chand, W.: Statistical approach to X-Ray CT imaging and its applications in image analysis—part II: a new stochastic model-based image segmentation technique for X-Ray CT image. IEEE Trans. Med. Imaging Volume 11(1), 1992, pp; 62–69.
  • Bezdek, J.C.; Hall, L.O.; Clark, M.C.; Gold gof, D.B.; Clarke, L.P.: Medical image analysis with fuzzy models. Stat. Methods Med. Res.Volume 6, 1997, pp; 191–214.
  • Kennedy J. and Eberhart R. C., “Particle Swarm optimization,”in Proceedings of IEEE International Conference on Neural Network, Piscataway, NJ, 1995, pp. 1942
  • M. Kass, A. Witkin, and D. Terzopoulos, “Snakes, active contour model,” International Journal of Computer Vision, pp.321-331, 1988.
  • S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys., vol. 79, no. 1, pp. 12–49, Nov. 1988V.
  • Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J.Comput. Vis., vol. 22, no. 1, pp. 61–79, Feb./Mar. 1997.
  • T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans.Image Process., vol. 10, no. 2, pp. 266–277, Feb. 2001.
  • M.Fatih Talu, “ORCAM: Online region-based active contour model”. www.elsevier.com/locate/eswa .Expert Systems with Applications 40 (2013) 6233–6240.
  • J. Kennedy, R. Eberhart, Particle swarm optimization, in:Proceedings IEEE International Conference on NeuralNetworks, vol. 4, Perth, Australia, 1995; pp. 1942–1948.
  • Shalov agarwal, shashank yadav and kanchan singh”K means Vs Kmeans ++clustering techniques” IEEE2012.
  • RamuduKama and RangababuTummala “Layers and Dark sand dunes segmentation of MARS satellite Imagery Using Level Set Model” IETE Journal, Vol.56, Issue 2, 2015, (ISSN:- 0974-7338), pp: 59-67
  • Shen, S.; Sandham, W. Granat, M.; Sterr, A.: MRI fuzzy segmentation of brain tissue using neighborhood attraction with eural-network optimization. IEEE Trans. Inf. Technol. Biomed. Volume 9(3), 2005, pp; 459–467.
  • Ramudu Kama, and RangababuTummala “Segmentation of Tissues from MRI Bio-medical Images Using Kernel Fuzzy PSO clustering based Level Set approach” Current Medical Imaging Reviews: An International Journal, Vol.13, Issue 1, 2017, ISSN (print):- 1573-4056 & ISSN (online):-1875-6603, pp: 1-12.( Indexed by SCI, Impact Factor: 0.933). DOI: 10.2174/1573405613666170123124652
  • Ramudu Kama and RangababuTummala “Level set evaluation of biomedical MRI and CT scan images using optimized fuzzy region clustering” Computer Methods in Biomechanics and Biomedical Engineering: Imaging &Visualization,Volume7,2018,https://doi.org/10.1080/21681163.2018.1441074
  • AbdenourMekhmoukh and KarimMokrani” Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation” Elsevier, computer methods and programs in biomed ic ine1 2 2 ( 2 0 1 5 ) 266–281
  • Ch Kalyani, Kama Ramudu and Ganta Raghotham Reddy “Optimized Segmentation of Tissues and Tumors in Medical Images using AFMKM Clustering via Level Set Formulation”. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 7 (2018) pp. 4989-4999
  • Ramudu Kama, Srinivas.A and RangababuTummala “Segmentation of Satellite and Medical Imagery Using Homomorphic filtering based Level Set Model” Indian Journal of Science & Technology, Vol 9(S1), DOI: 10.17485/ijst/2016/v9iS1/107818, December 2016, ISSN (Print) : 0974-6846 &ISSN (Online) : 0974-5645
  • http://www.ctisus.com/responsive/teachingfiles
  • http://brainweb.bic.mni.mcgill.ca/.
Еще
Статья научная