A comprehensive study on image segmentation

  • Authors

    • J. maruthi nagendra prasad assistant professor,cse deptindia
    • Dr. M.VamsiKrishna Head of the Department, Computer Science and Engineering, Centurion University
    2018-11-15
    https://doi.org/10.14419/ijet.v7i4.13689
  • Image Segmentation, Thresholding, Clustering, Soft Computing Approaches, Region-Based Segmentation.

  • With the evolution of computing, there is an acceleration in the use of image processing techniques in various applications, image segmentation, a procedure in which images are divided into many segments and with this, it’s possible to identify the region of interest from an image. The main idea of this comprehensive study is to present various existing segmentation techniques.

     

     

     

     


  • References

    1. [1] Sharif, M., Mohsin, S., Jamal, M. J., & Raza, M. (2010, July). Illumination Normalization Preprocessing for face recognition. In Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on (Vol. 2, pp. 44-47). IEEE.

      [2] Yasmin, M., Sharif, M., Masood, S., Raza, M., & Mohsin, S. (2012). Brain image enhancement-A survey. World Applied Sciences Journal, 17(9), 1192-1204.

      [3] Rehman, M., Iqbal, M., Sharif, M., & Raza, M. (2012). Content based image retrieval: survey. World Applied Sciences Journal, 19(3), 404-412.

      [4] Yasmin, M., Mohsin, S., Irum, I., & Sharif, M. (2013). Content based image retrieval by shape, color and relevance feedback. Life Science Journal, 10(4s), 593-598.

      [5] Liu, C., Ng, M. K. P., & Zeng, T. (2018). Weighted variational model for selective image segmentation with application to medical images. Pattern Recognition, 76, 367-379.

      [6] Gao, Y., Li, X., Dong, M., & Li, H. P. (2018). An enhanced artificial bee colony optimizer and its application to multi-level threshold image segmentation. Journal of Central South University, 25(1), 107-120.

      [7] Yu, X., Zhou, Z., Gao, Q., Li, D., & Ríha, K. (2018). Infrared image segmentation using growing immune field and clone threshold. Infrared Physics & Technology, 88, 184-193.

      [8] Li, J., Tang, W., Wang, J., & Zhang, X. (2018). Multilevel thresholding selection based on variational mode decomposition for image segmentation. Signal Processing, 147, 80-91.

      [9] Fisher, F. Fast threshold image segmentation based on 2D Fuzzy Fisher and Random Local Optimized QPSO.

      [10] Han, J., Yang, C., Zhou, X., & Gui, W. (2017). A new multi-threshold image segmentation approach using state transition algorithm. Applied Mathematical Modelling, 44, 588-601.

      [11] Khairuzzaman, A. K. M., & Chaudhury, S. (2017). Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Systems with Applications, 86, 64-76

      [12] Li, Z., Yu, Z., Liu, W., & Zhang, Z. (2017, July). Tongue image segmentation via color decomposition and thresholding. In Information Science and Control Engineering (ICISCE), 2017 4th International Conference on (pp. 752-755). IEEE.

      [13] Chang, B. Y. (2017). U.S. Patent No. 9,846,816. Washington, DC: U.S. Patent and Trademark Office.

      [14] Feng, Y., Zhao, H., Li, X., Zhang, X., & Li, H. (2017). A multi-scale 3D Otsu thresholding algorithm for medical image segmentation. Digital Signal Processing, 60, 186-199.

      [15] He, L., & Huang, S. (2017). Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing, 240, 152-174.

      [16] R. Kumar et al., “Histogram Thresholding in Image Segmentation: A Joint Level Set Method and Lattice Boltzmann Method Based Approach,†2017, pp. 529–539.

      [17] M. A. El Aziz, A. A. Ewees, and A. E. Hassanien, “Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation,†Expert Syst. Appl., vol. 83, pp. 242–256, Oct. 2017.

      [18] A. Singla and S. Patra, “A fast automatic optimal threshold selection technique for image segmentation,†Signal, Image Video Process., vol. 11, no. 2, pp. 243–250, Feb. 2017.

      [19] Na, L., Yan, J., & Shu, L. (2017, October). Application of PSO algorithm with dynamic inertia weight in medical image thresholding segmentation. In e-Health Networking, Applications and Services (Healthcom), 2017 IEEE 19th International Conference on (pp. 1-4). IEEE.

      [20] H. Zhu, Z. Zhuang, J. Zhou, F. Zhang, X. Wang, and Y. Wu, “Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization,†Multimed. Tools Appl., vol. 76, no. 6, pp. 8951–8968, Mar. 2017.

      [21] Sehgal, S., Kumar, S., & Bindu, M. H. (2017, January). Remotely sensed image thresholding using OTSU & differential evolution approach. In Cloud Computing, Data Science & Engineering-Confluence, 2017 7th International Conference on (pp. 138-142). IEEE.

      [22] Sarkar, S., Das, S., & Chaudhuri, S. S. (2016). Hyper-spectral image segmentation using Rényi entropy based multi-level thresholding aided with differential evolution. Expert Systems with Applications, 50, 120-129.

      [23] Zhao, X., Turk, M., Li, W., Lien, K. C., & Wang, G. (2016). A multilevel image thresholding segmentation algorithm based on two-dimensional K–L divergence and modified particle swarm optimization. Applied Soft Computing, 48, 151-159.

      [24] H. Deng, J. P. Fitts, and C. A. Peters, “Quantifying fracture geometry with X-ray tomography: Technique of Iterative Local Thresholding (TILT) for 3D image segmentation,†Comput. Geosci., vol. 20, no. 1, pp. 231–244, Feb. 2016.

      [25] A. Mostafa, M. A. Elfattah, A. Fouad, A. E. Hassanien, and H. Hefny, “Wolf Local Thresholding Approach for Liver Image Segmentation in CT Images,†2016, pp. 641–651.

      [26] Bhandari, A. K., Kumar, A., & Singh, G. K. (2015). Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Systems with Applications, 42(3), 1573-1601.

      [27] Li, Y., Jiao, L., Shang, R., & Stolkin, R. (2015). Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Information Sciences, 294, 408-422.

      [28] Bhandari, A. K., Kumar, A., & Singh, G. K. (2015). Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Systems with Applications, 42(22), 8707-8730.

      [29] Ayala, H. V. H., dos Santos, F. M., Mariani, V. C., & dos Santos Coelho, L. (2015). Image thresholding segmentation based on a novel beta differential evolution approach. Expert Systems with Applications, 42(4), 2136-2142.

      [30] Y. Liu, C. Mu, W. Kou, and J. Liu, “Modified particle swarm optimization-based multilevel thresholding for image segmentation,†Soft Comput., vol. 19, no. 5, pp. 1311–1327, May 2015.

      [31] Aja-Fernández, S., Curiale, A. H., & Vegas-Sánchez-Ferrero, G. (2015). A local fuzzy thresholding methodology for multiregion image segmentation. Knowledge-Based Systems, 83, 1-12.

      [32] Liu, L., Yang, N., Lan, J., & Li, J. (2015). Image segmentation based on gray stretch and threshold algorithm. Optik-International Journal for Light and Electron Optics, 126(6), 626-629.

      [33] Zhou, C., Tian, L., Zhao, H., & Zhao, K. (2015, June). A method of two-dimensional Otsu image threshold segmentation based on improved firefly algorithm. In Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on (pp. 1420-1424). IEEE.

      [34] V. Rajinikanth, S. L. Fernandes, B. Bhushan, Harisha, and N. R. Sunder, “Segmentation and Analysis of Brain Tumor Using Tsallis Entropy and Regularised Level Set,†2018, pp. 313–321.

      [35] D. Oliva, S. Hinojosa, M. A. Elaziz, and N. Ortega-Sánchez, “Context based image segmentation using antlion optimization and sine cosine algorithm,†Multimed. Tools Appl., Mar. 2018.

      [36] Al-Amri, S. S., & Kalyankar, N. V. (2010). Image segmentation by using threshold techniques. arXiv preprint arXiv:1005.4020.

      [37] Wei, K., Zhang, T., Shen, X., & Liu, J. (2007, August). An improved threshold selection algorithm based on particle swarm optimization for image segmentation. In Natural Computation, 2007. ICNC 2007. Third International Conference on (Vol. 5, pp. 591-594). IEEE.

      [38] Sharif, M., Ali, M. A., Raza, M., & Mohsin, S. (2015). Face recognition using edge information and DCT. Sindh University Research Journal-SURJ (Science Series), 43(2).

      [39] Lakshmi, S., & Sankaranarayanan, D. V. (2010). A study of edge detection techniques for segmentation computing approaches. IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications†CASCT, 35-40.

      [40] Yu, X., & Yla-Jaaski, J. (1991, June). A new algorithm for image segmentation based on region growing and edge detection. In Circuits and Systems, 1991, IEEE International Sympoisum on (pp. 516-519). IEEE.

      [41] Yang, B., Xiang, D., Yu, F., & Chen, X. (2018, March). Lung tumor segmentation based on multi-scale template matching and region growing. In Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 10578, p. 105782Q). International Society for Optics and Photonics.

      [42] Patel, M., Kelly, P., Smith, M., Plassard, A., Landman, B., Abramsom, R. G., & Asman, A. J. (2018). U.S. Patent Application No. 15/540,487.

      [43] Wang, F., Wu, Y., Li, M., Zhang, P., & Zhang, Q. (2017). Adaptive Hybrid Conditional Random Field Model for SAR Image Segmentation. IEEE Trans. Geoscience and Remote Sensing, 55(1), 537-550.

      [44] Chen, C., Zare, A., & Cobb, J. T. (2016, December). Partial membership latent dirichlet allocation for image segmentation. In Pattern Recognition (ICPR), 2016 23rd International Conference on (pp. 2368-2373). IEEE.

      [45] Oh, C., Ham, B., & Sohn, K. (2017). Robust interactive image segmentation using structure-aware labeling. Expert Systems with Applications, 79, 90-100.

      [46] Wang, M., Huang, J., & Ming, D. (2017). Region-line association constraints for high-resolution image segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(2), 628-637.

      [47] K. Chowdhury, D. Chaudhuri, and A. K. Pal, “A new image segmentation technique using bi-entropy function minimization,†Multimed. Tools Appl., Dec. 2017.

      [48] J. Lian et al., “Automatic gallbladder and gallstone regions segmentation in ultrasound image,†Int. J. Comput. Assist. Radiol. Surg., vol. 12, no. 4, pp. 553–568, Apr. 2017.

      [49] Baghi, A., & Karami, A. (2017, April). SAR image segmentation using region growing and spectral cluster. In Pattern Recognition and Image Analysis (IPRIA), 2017 3rd International Conference on (pp. 229-232). IEEE.

      [50] Parida, P., & Bhoi, N. (2017). 2-D Gabor filter based transition region extraction and morphological operation for image segmentation. Computers & Electrical Engineering, 62, 119-134.

      [51] Li, Z., Liu, G., Zhang, D., & Xu, Y. (2016). Robust single-object image segmentation based on salient transition region. Pattern recognition, 52, 317-331.

      [52] Y. Li et al., “In-field cotton detection via region-based semantic image segmentation,†Elsevier.

      [53] Shih, H. C., & Liu, E. R. (2016). New quartile-based region merging algorithm for unsupervised image segmentation using color-alone feature. Information Sciences, 342, 24-36.

      [54] Medeiros, R. S., Scharcanski, J., & Wong, A. (2016). Image segmentation via multi-scale stochastic regional texture appearance models. Computer Vision and Image Understanding, 142, 23-36.

      [55] Kaganami, H. G., & Beiji, Z. (2009, September). Region-based segmentation versus edge detection. In Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP'09. Fifth International Conference on (pp. 1217-1221). IEEE.

      [56] Karoui, I., Fablet, R., Boucher, J. M., & Augustin, J. M. (2007, October). Unsupervised region-based image segmentation using texture statistics and level-set methods. In Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on (pp. 1-5). IEEE.

      [57] Zhou, Y. M., Jiang, S. Y., & Yin, M. L. (2008, October). A region-based image segmentation method with mean-shift clustering algorithm. In Fuzzy Systems and Knowledge Discovery, 2008. FSKD'08. Fifth International Conference on (Vol. 2, pp. 366-370). IEEE.

      [58] Cigla, C., & Alatan, A. A. (2008, October). Region-based image segmentation via graph cuts. In Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on (pp. 2272-2275). IEEE.

      [59] Wesolkowski, S., & Fieguth, P. (2002). A Markov random fields model for hybrid edge-and region-based color image segmentation. In Electrical and Computer Engineering, 2002. IEEE CCECE 2002. Canadian Conference on (Vol. 2, pp. 945-949). IEEE.

      [60] Sharif, M., Shah, J. H., Mohsin, S., & Raza, M. (2013). Subholistic hidden markov model for face recognition. Research Journal of Recent Sciences, 2277, 2502.

      [61] Hsiao, Y. T., Chuang, C. L., Jiang, J. A., & Chien, C. C. (2005, October). A contour based image segmentation algorithm using morphological edge detection. In Systems, Man and Cybernetics, 2005 IEEE International Conference on (Vol. 3, pp. 2962-2967). IEEE.

      [62] Haider, W., Malik, M. S., Raza, M., Wahab, A., Khan, I. A., Zia, U., & Bashir, H. (2012). A hybrid method for edge continuity based on Pixel Neighbors Pattern Analysis (PNPA) for remote sensing satellite images. Int'l J. of Communications, Network and System Sciences, 5(29), 624-630.

      [63] Zaim, A. (2008, March). An edge-based approach for segmentation of prostate ultrasouind images using phase symmetry. In Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on (pp. 10-13). IEEE.

      [64] Ding, K., Xiao, L., & Weng, G. (2018). Active contours driven by local pre-fitting energy for fast image segmentation. Pattern Recognition Letters, 104, 29-36.

      [65] H. Yu, F. He, and Y. Pan, “A novel region-based active contour model via local patch similarity measure for image segmentation,†Multimed. Tools Appl., Feb. 2018.

      [66] Y. Li, G. Cao, Q. Yu, and X. Li, “Fast and Robust Active Contours Model for Image Segmentation,†Neural Process. Lett., Mar. 2018.

      [67] Li, X., Wang, X., & Dai, Y. (2018). Adaptive Energy Weight Based Active Contour Model for Robust Medical Image Segmentation. Journal of Signal Processing Systems, 90(3), 449-465.

      [68] S. Niu et al., “Robust noise region-based active contour model via local similarity factor for image segmentation,†Elsevier.

      [69] Zhu, Y., Hao, B., Jiang, B., Nian, R., He, B., Ren, X., & Lendasse, A. (2017, June). Underwater image segmentation with co-saliency detection and local statistical active contour model. In OCEANS 2017-Aberdeen (pp. 1-5). IEEE.

      [70] Pratondo, A., Chui, C. K., & Ong, S. H. (2017). Integrating machine learning with region-based active contour models in medical image segmentation. Journal of Visual Communication and Image Representation, 43, 1-9.

      [71] Khadidos, A., Sanchez, V., & Li, C. T. (2017). Weighted level set evolution based on local edge features for medical image segmentation. IEEE Transactions on Image Processing, 26(4), 1979-1991.

      [72] Gao, G., Wen, C., & Wang, H. (2017). Fast and robust image segmentation with active contours and Student's-t mixture model. Pattern Recognition, 63, 71-86.

      [73] L. Wang et al., “Active contours driven by edge entropy fitting energy for image segmentation,†Elsevier.

      [74] Mesadi, F., Çetin, M., & Tasdizen, T. (2017). Disjunctive Normal Parametric Level Set With Application to Image Segmentation. IEEE Trans. Image Processing, 26(6), 2618-2631.

      [75] Liu, C., Liu, W., & Xing, W. (2017). An improved edge-based level set method combining local regional fitting information for noisy image segmentation. Signal Processing, 130, 12-21.

      [76] Liu, Y., He, C., Wu, Y., & Ren, Z. (2018). The L0-regularized discrete variational level set method for image segmentation. Image and Vision Computing, 75, 32-43.

      [77] L. Liu, D. Cheng, F. Tian, D. Shi, and R. Wu, “Active contour driven by multi-scale local binary fitting and Kullback-Leibler divergence for image segmentation,†Multimed. Tools Appl., vol. 76, no. 7, pp. 10149–10168, Apr. 2017.

      [78] Ding, K., Xiao, L., & Weng, G. (2017). Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation. Signal Processing, 134, 224-233.

      [79] Huo, G., Yang, S. X., Li, Q., & Zhou, Y. (2017). A robust and fast method for sidescan sonar image segmentation using nonlocal despeckling and active contour model. IEEE transactions on cybernetics, 47(4), 855-872.

      [80] Fu, X., Chen, C., Li, J., Wang, C., & Kuo, C. C. J. (2017, September). Image segmentation using contour, surface, and depth cues. In Image Processing (ICIP), 2017 IEEE International Conference on (pp. 81-85). IEEE.

      [81] A. E. Rad, M. S. Mohd Rahim, H. Kolivand, and I. Bin Mat Amin, “Morphological region-based initial contour algorithm for level set methods in image segmentation,†Multimed. Tools Appl., vol. 76, no. 2, pp. 2185–2201, Jan. 2017.

      [82] Kashyap, R., & Tiwari, V. (2017). Energy-based active contour method for image segmentation. International Journal of Electronic Healthcare, 9(2-3), 210-225.

      [83] [83] Zhang, H., Zuo, W., Wang, K., & Zhang, D. (2006). A snakeâ€based approach to automated segmentation of tongue image using polar edge detector. International Journal of Imaging Systems and Technology, 16(4), 103-112.

      [84] Du, Y., & Du, D. (2017, July). Cardiac image segmentation using generalized polynomial chaos expansion and level set function. In Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE (pp. 652-655). IEEE.

      [85] Zhou, S., Wang, J., Zhang, S., Liang, Y., & Gong, Y. (2016). Active contour model based on local and global intensity information for medical image segmentation. Neurocomputing, 186, 107-118.

      [86] Guo, Y., Şengür, A., & Tian, J. W. (2016). A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set. Computer methods and programs in biomedicine, 123, 43-53.

      [87] Pratondo, A., Chui, C. K., & Ong, S. H. (2016). Robust edge-stop functions for edge-based active contour models in medical image segmentation. IEEE Signal Processing Letters, 23(2), 222-226.

      [88] Song, Y., Wu, Y., & Dai, Y. (2016). A new active contour remote sensing river image segmentation algorithm inspired from the cross entropy. Digital Signal Processing, 48, 322-332.

      [89] Shi, N., & Pan, J. (2016). An improved active contours model for image segmentation by level set method. Optik-International Journal for Light and Electron Optics, 127(3), 1037-1042.

      [90] Yang, X., Gao, X., Tao, D., Li, X., & Li, J. (2015). An efficient MRF embedded level set method for image segmentation. IEEE transactions on image processing, 24(1), 9-21.

      [91] Wu, Q., Gan, Y., Lin, B., Zhang, Q., & Chang, H. (2015). An active contour model based on fused texture features for image segmentation. Neurocomputing, 151, 1133-1141.

      [92] H. Min et al., “An intensity-texture model based level set method for image segmentation,†Elsevier.

      [93] Wang, X. F., Min, H., Zou, L., & Zhang, Y. G. (2015). A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement. Pattern Recognition, 48(1), 189-204.

      [94] Abdelsamea, M. M., Gnecco, G., & Gaber, M. M. (2015). An efficient Self-Organizing Active Contour model for image segmentation. Neurocomputing, 149, 820-835.

      [95] Ji, Z., Xia, Y., Sun, Q., Cao, G., & Chen, Q. (2015). Active contours driven by local likelihood image fitting energy for image segmentation. Information Sciences, 301, 285-304.

      [96] Wang, X. F., Min, H., & Zhang, Y. G. (2015). Multi-scale local region based level set method for image segmentation in the presence of intensity inhomogeneity. Neurocomputing, 151, 1086-1098.

      [97] Wu, Y., & He, C. (2015). A convex variational level set model for image segmentation. Signal Processing, 106, 123-133.

      [98] Dai, L., Ding, J., & Yang, J. (2015). Inhomogeneity-embedded active contour for natural image segmentation. Pattern Recognition, 48(8), 2513-2529.

      [99] Ge, Q., Li, C., Shao, W., & Li, H. (2015). A hybrid active contour model with structured feature for image segmentation. Signal Processing, 108, 147-158.

      [100] M. M. Abdelsamea, G. Gnecco, and M. Medhat Gaber, “A SOM-based Chan–Vese model for unsupervised image segmentation,†Soft Comput., vol. 21, no. 8, pp. 2047–2067, Apr. 2017.

      [101] Zhu, S., & Gao, R. (2016). A novel generalized gradient vector flow snake model using minimal surface and component-normalized method for medical image segmentation. Biomedical Signal Processing and Control, 26, 1-10.

      [102] Jiang, X., Zhang, R., & Nie, S. (2009, June). Image segmentation based on PDEs model: A survey. In Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009. 3rd International Conference on (pp. 1-4). IEEE.

      [103] Bueno, S. G., Martinez-Albala, A., & Cosfas, P. (2004, October). Fuzziness and PDE based models for the segmentation of medical image. In Nuclear Science Symposium Conference Record, 2004 IEEE (Vol. 6, pp. 3777-3780). IEEE.

      [104] Shahzad, A., Sharif, M., Raza, M., & Hussain, K. (2008). Enhanced watershed image processing segmentation. Journal of Information & Communication Technology, 2(1), 01-09.

      [105] Chen, S., Sun, T., Yang, F., Sun, H., & Guan, Y. (2018). An improved optimum-path forest clustering algorithm for remote sensing image segmentation. Computers & Geosciences, 112, 38-46.

      [106] Jiao, Y., Wu, J., & Jiao, L. (2018). An image segmentation method based on network clustering model. Physica A: Statistical Mechanics and its Applications, 490, 1532-1542.

      [107] Gui, L., He, L., Ni, Z., & Hong, T. (2018). Visualized image segmentation for multi-object tracking by weak clustering technique. Multimedia Tools and Applications, 1-9.

      [108] Y. Guo, R. Xia, A. Şengür, and K. Polat, “A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering,†Neural Comput. Appl., vol. 28, no. 10, pp. 3009–3019, Oct. 2017.

      [109] M. Shehab, M. Al-Ayyoub, Y. Jararweh, and M. Jarrah, “Accelerating compute-intensive image segmentation algorithms using GPUs,†J. Supercomput., vol. 73, no. 5, pp. 1929–1951, May 2017.

      [110] Chen, Q., Yuen, K. K. F., & Guan, C. (2017, June). Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. In Developments in eSystems Engineering (DeSE), 2017 10th International Conference on (pp. 238-241). IEEE.

      [111] Feng, C., Zhao, D., & Huang, M. (2017). Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): a region-based level set method. Neurocomputing, 219, 107-129.

      [112] X. X. Li, X. J. Shen, H. P. Chen, and Y. C. Feng, “Image clustering segmentation based on SLIC superpixel and transfer learning,†Pattern Recognit. Image Anal., vol. 27, no. 4, pp. 838–845, Oct. 2017.

      [113] Hou, J., Liu, W., Xu, E., & Cui, H. (2016). Towards parameter-independent data clustering and image segmentation. Pattern Recognition, 60, 25-36.

      [114] Ayech, M. W., & Ziou, D. (2016). Terahertz image segmentation using k-means clustering based on weighted feature learning and random pixel sampling. Neurocomputing, 175, 243-264.

      [115] Hasnat, M. A., Alata, O., & Tremeau, A. (2016). Joint color-spatial-directional clustering and region merging (JCSD-RM) for unsupervised RGB-D image segmentation. IEEE transactions on pattern analysis and machine intelligence, 38(11), 2255-2268.

      [116] Samundeeswari, E. S., Saranya, P. K., & Manavalan, R. (2016, March). Segmentation of breast ultrasound image using regularized K-means (ReKM) clustering. In Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on (pp. 1379-1383). IEEE.

      [117] Yang, Y., Wang, Y., & Xue, X. (2016). A novel spectral clustering method with superpixels for image segmentation. Optik-International Journal for Light and Electron Optics, 127(1), 161-167.

      [118] Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54, 764-771.

      [119] Li, H., He, H., & Wen, Y. (2015). Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation. Optik-International Journal for Light and Electron Optics, 126(24), 4817-4822.

      [120] Abdel-Maksoud, E., Elmogy, M., & Al-Awadi, R. (2015). Brain tumor segmentation based on a hybrid clustering technique. Egyptian Informatics Journal, 16(1), 71-81.

      [121] Pham, V. H., & Lee, B. R. (2015). An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm. Vietnam Journal of Computer Science, 2(1), 25-33.

      [122] Qin, F., Guo, J., & Lang, F. (2015). Superpixel segmentation for polarimetric SAR imagery using local iterative clustering. IEEE Geoscience and Remote Sensing Letters, 12(1), 13-17.

      [123] Huang, C., & Zeng, L. (2015). Robust image segmentation using local robust statistics and correntropy-based K-means clustering. Optics and Lasers in Engineering, 66, 187-203.

      [124] Saglam, A., & Baykan, N. A. (2017). Sequential image segmentation based on minimum spanning tree representation. Pattern Recognition Letters, 87, 155-162.

      [125] Lu, S., Wang, S., & Zhang, Y. (2017). A note on the marker-based watershed method for X-ray image segmentation. Computer methods and programs in biomedicine, 141, 1-2.

      [126] Avinash, S., Manjunath, K., & Kumar, S. S. (2016, August). An improved image processing analysis for the detection of lung cancer using Gabor filters and watershed segmentation technique. In Inventive Computation Technologies (ICICT), International Conference on (Vol. 3, pp. 1-6). IEEE.

      [127] Aparajeeta, J., Mahakud, S., Nanda, P. K., & Das, N. (2018). Variable Variance Adaptive Mean-Shift and possibilistic fuzzy C-means based recursive framework for brain MR image segmentation. Expert Systems with Applications, 92, 317-333.

      [128] F. Huang et al., “Implementation of the parallel mean shift-based image segmentation algorithm on a GPU cluster,†Int. J. Digit. Earth, pp. 1–26, Feb. 2018.

      [129] Bi, H., Tang, H., Yang, G., Shu, H., & Dillenseger, J. L. (2017). Accurate image segmentation using Gaussian mixture model with saliency map. Pattern Analysis and Applications, 1-10.

      [130] Zhou, Y., & Zhu, H. (2018). Image Segmentation Using a Trimmed Likelihood Estimator in the Asymmetric Mixture Model Based on Generalized Gamma and Gaussian Distributions. Mathematical Problems in Engineering, 2018.

      [131] Liu, Y., He, C., & Wu, Y. (2018). Variational model with kernel metric-based data term for noisy image segmentation. Digital Signal Processing, 78, 42-55.

      [132] Liu, F., Lin, G., Qiao, R., & Shen, C. (2017). Structured learning of tree potentials in CRF for imagesegmentation. IEEE transactions on neural networks and learning systems.

      [133] Pereyra, M., & McLaughlin, S. (2017). Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation. IEEE Transactions on Image Processing, 26(6), 2577-2587.

      [134] Wu, Y., Li, M., Zhang, Q., & Liu, Y. (2018). A Retinex modulated piecewise constant variational model for image segmentation and bias correction. Applied Mathematical Modelling, 54, 697-709.

      [135] Aydin, I., Karaköse, M., Hamsin, G. G., & Akin, E. (2017, September). A vision based inspection system using gaussian mixture model based interactive segmentation. In Artificial Intelligence and Data Processing Symposium (IDAP), 2017 International (pp. 1-4). IEEE.

      [136] Chandra, S., & Kokkinos, I. (2016, October). Fast, exact and multi-scale inference for semantic image segmentation with deep gaussian crfs. In European Conference on Computer Vision (pp. 402-418). Springer, Cham.

      [137] Dong, X., Shen, J., Shao, L., & Van Gool, L. (2016). Sub-Markov random walk for image segmentation. IEEE Transactions on Image Processing, 25(2), 516-527.

      [138] Jian, M., & Jung, C. (2016). Interactive image segmentation using adaptive constraint propagation. IEEE transactions on image processing, 25(3), 1301-1311.

      [139] Xia, Y., Ji, Z., & Zhang, Y. (2016). Brain MRI image segmentation based on learning local variational Gaussian mixture models. Neurocomputing, 204, 189-197.

      [140] Zhao, L., Li, K., Wang, M., Yin, J., Zhu, E., Wu, C., & Zhu, C. (2016). Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF. Computers in biology and medicine, 71, 46-56.

      [141] Ahmadvand, A., & Kabiri, P. (2016). Multispectral MRI image segmentation using Markov random field model. Signal, Image and Video Processing, 10(2), 251-258.

      [142] De, A., Zhang, Y., & Guo, C. (2016). A parallel adaptive segmentation method based on SOM and GPU with application to MRI image processing. Neurocomputing, 198, 180-189.

      [143] Liu, F., Duan, Y., Li, L., Jiao, L., Wu, J., Yang, S., & Yuan, J. (2016). SAR image segmentation based on hierarchical visual semantic and adaptive neighborhood multinomial latent model. IEEE Transactions on Geoscience and Remote Sensing, 54(7), 4287-4301.

      [144] Kittaneh, O. A., Khan, M. A., Akbar, M., & Bayoud, H. A. (2016). Average entropy: a new uncertainty measure with application to image segmentation. The American Statistician, 70(1), 18-24.

      [145] Zhang, K., Liu, Q., Song, H., & Li, X. (2015). A variational approach to simultaneous image segmentation and bias correction. IEEE Transactions on Cybernetics, 45(8), 1426-1437.

      [146] M. Xie, J. Gao, C. Zhu, and Y. Zhou, “A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity,†Med. Biol. Eng. Comput., vol. 53, no. 1, pp. 23–35, Jan. 2015.

      [147] Mesadi, F., Erdil, E., Cetin, M., & Tasdizen, T. (2018). Image segmentation using disjunctive normal Bayesian shape and appearance models. IEEE transactions on medical imaging, 37(1), 293-305.

      [148] Yu, H., Zhou, Y., Qian, H., Xian, M., & Wang, S. (2017, September). Loosecut: interactive image segmentation with loosely bounded boxes. In Image Processing (ICIP), 2017 IEEE International Conference on (pp. 3335-3339). IEEE.

      [149] Moriya, T., Roth, H. R., Nakamura, S., Oda, H., Nagara, K., Oda, M., & Mori, K. (2018, March). Unsupervised pathology image segmentation using representation learning with spherical k-means. In Medical Imaging 2018: Digital Pathology (Vol. 10581, p. 1058111). International Society for Optics and Photonics.

      [150] Pham, T. X., Siarry, P., & Oulhadj, H. (2018). Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Applied Soft Computing, 65, 230-242.

      [151] S. S. Chinta, A. Jain, and B. K. Tripathy, “Image Segmentation Using Hybridized Firefly Algorithm and Intuitionistic Fuzzy C-Means,†2018, pp. 651–659.

      [152] Y Liu, Y., Chen, Y., Han, B., Zhang, Y., Zhang, X., & Su, Y. (2018). Fully automatic Breast ultrasound image segmentation based on fuzzy cellular automata framework. Biomedical Signal Processing and Control, 40, 433-442.

      [153] Liu, H., Zhao, F., & Chaudhary, V. (2018). Pareto-based interval type-2 fuzzy c-means with multi-scale JND color histogram for image segmentation. Digital Signal Processing, 76, 75-83.

      [154] Modi, H., Baraiya, N., & Patel, H. (2018). Comparative Analysis of Segmentation of Tumor from Brain MRI Images Using Fuzzy C-Means and K-Means. Fuzzy Systems, 10(1), 14-18.

      [155] Wan, C., Yuan, X., Dai, X., Zhang, T., & He, Q. (2018). A self-adaptive multi-objective harmony search based fuzzy clustering technique for image segmentation. Journal of Ambient Intelligence and Humanized Computing, 1-16.

      [156] Rundo, L., Militello, C., Russo, G., D’Urso, D., Valastro, L. M., Garufi, A., & Gilardi, M. C. (2018). Fully Automatic Multispectral MR Image Segmentation of Prostate Gland Based on the Fuzzy C-Means Clustering Algorithm. In Multidisciplinary Approaches to Neural Computing (pp. 23-37). Springer, Cham.

      [157] X. Zhao, Y. Li, and Q. Zhao, “A Fuzzy Clustering Approach for Complex Color Image Segmentation Based on Gaussian Model with Interactions between Color Planes and Mixture Gaussian Model,†Int. J. Fuzzy Syst., vol. 20, no. 1, pp. 309–317, Jan. 2018.

      [158] Senthilkumar, C., & Gnanamurthy, R. K. (2018). A Fuzzy clustering based MRI brain image segmentation using back propagation neural networks. Cluster Computing, 1-8.

      [159] Choy, S. K., Lam, S. Y., Yu, K. W., Lee, W. Y., & Leung, K. T. (2017). Fuzzy model-based clustering and its application in image segmentation. Pattern Recognition, 68, 141-157.

      [160] Zhao, Q. H., Li, X. L., Li, Y., & Zhao, X. M. (2017). A fuzzy clustering image segmentation algorithm based on hidden Markov random field models and Voronoi tessellation. Pattern Recognition Letters, 85, 49-55.

      [161] X. Zhang, G. Wang, Q. Su, Q. Guo, C. Zhang, and B. Chen, “An improved fuzzy algorithm for image segmentation using peak detection, spatial information and reallocation,†Soft Comput., vol. 21, no. 8, pp. 2165–2173, Apr. 2017.

      [162] Namburu, A., kumar Samay, S., & Edara, S. R. (2017). Soft fuzzy rough set-based MR brain image segmentation. Applied Soft Computing, 54, 456-466.

      [163] Santi, D. N., Mahfudh, A. A., & Soeleman, M. A. (2017, October). Image enhancement segmentation Indonesian's Batik based on fuzzy C-means clustering using median filter. In Application for Technology of Information and Communication (iSemantic), 2017 International Seminar on (pp. 1-4). IEEE.

      [164] Amato, F., Barbareschi, M., Cozzolino, G., Mazzeo, A., Mazzocca, N., & Tammaro, A. (2017, September). Outperforming Image Segmentation by Exploiting Approximate K-Means Algorithms. In International Conference on Optimization and Decision Science (pp. 31-38). Springer, Cham.

      [165] J. Shi, Y. Lei, J. Wu, A. Paul, M. Kim, and G. Jeon, “Uncertain clustering algorithms based on rough and fuzzy sets for real-time image segmentation,†J. Real-Time Image Process., vol. 13, no. 3, pp. 645–663, Sep. 2017.

      [166] Zhang, M., Jiang, W., Zhou, X., Xue, Y., & Chen, S. (2017). A hybrid biogeography-based optimization and fuzzy C-means algorithm for image segmentation. Soft Computing, 1-14.

      [167] Aghajari, E., & Chandrashekhar, G. D. (2017). Self-organizing map based extended fuzzy c-means (SEEFC) algorithm for image segmentation. Applied Soft Computing, 54, 347-363.

      [168] X. Zhang, Y. Sun, G. Wang, Q. Guo, C. Zhang, and B. Chen, “Improved fuzzy clustering algorithm with non-local information for image segmentation,†Multimed. Tools Appl., vol. 76, no. 6, pp. 7869–7895, Mar. 2017.

      [169] Gharieb, R. R., Gendy, G., & Abdelfattah, A. (2017). C-means clustering fuzzified by two membership relative entropy functions approach incorporating local data information for noisy image segmentation. Signal, Image and Video Processing, 11(3), 541-548.

      [170] Wan, L., Zhang, T., Xiang, Y., & You, H. (2018). A Robust Fuzzy C-Means Algorithm Based on Bayesian Nonlocal Spatial Information for SAR Image Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3), 896-906.

      [171] Koundal, D., Anand, V., & Bhat, S. (2017, December). Comparative analysis of neutrosophic and intuitionistic fuzzy set with spatial information on image segmentation. In Image Information Processing (ICIIP), 2017 Fourth International Conference on (pp. 1-5). IEEE.

      [172] Feng, C., Zhao, D., & Huang, M. (2016). Image segmentation using CUDA accelerated non-local means denoising and bias correction embedded fuzzy c-means (BCEFCM). Signal processing, 122, 164-189.

      [173] Bose, A., & Mali, K. (2016). Fuzzy-based artificial bee colony optimization for gray image segmentation. Signal, Image and Video Processing, 10(6), 1089-1096.

      [174] Verma, H., Agrawal, R. K., & Sharan, A. (2016). An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation. Applied Soft Computing, 46, 543-557.

      [175] Shang, R., Tian, P., Jiao, L., Stolkin, R., Feng, J., Hou, B., & Zhang, X. (2016). A spatial fuzzy clustering algorithm with kernel metric based on immune clone for SAR image segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(4), 1640-1652.

      [176] Tuan, T. M., & Ngan, T. T. (2016). A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental X-ray image segmentation. Applied Intelligence, 45(2), 402-428.

      [177] Jiang, X. L., Wang, Q., He, B., Chen, S. J., & Li, B. L. (2016). Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints. Neurocomputing, 207, 22-35.

      [178] Rajaby, E., Ahadi, S. M., & Aghaeinia, H. (2016). Robust color image segmentation using fuzzy c-means with weighted hue and intensity. Digital Signal Processing, 51, 170-183.

      [179] Shahverdi, R., Tavana, M., Ebrahimnejad, A., Zahedi, K., & Omranpour, H. (2016). An improved method for edge detection and image segmentation using fuzzy cellular automata. Cybernetics and Systems, 47(3), 161-179.

      [180] Zhu, H., & Pan, X. (2016). Robust fuzzy clustering using nonsymmetric student׳ st finite mixture model for MR image segmentation. Neurocomputing, 175, 500-514.

      [181] Sarkar, J. P., Saha, I., & Maulik, U. (2016). Rough possibilistic type-2 fuzzy C-means clustering for MR brain image segmentation. Applied Soft Computing, 46, 527-536.

      [182] Li, X., Song, J., Zhang, F., Ouyang, X., & Khan, S. U. (2016). MapReduce-based fast fuzzy c-means algorithm for large-scale underwater image segmentation. Future Generation Computer Systems, 65, 90-101.

      [183] Zhao, F., Liu, H., & Fan, J. (2015). A multiobjective spatial fuzzy clustering algorithm for image segmentation. Applied Soft Computing, 30, 48-57.

      [184] Gómez, D., Yáñez, J., Guada, C., Rodríguez, J. T., Montero, J., & Zarrazola, E. (2015). Fuzzy image segmentation based upon hierarchical clustering. Knowledge-Based Systems, 87, 26-37.

      [185] Liu, G., Zhang, Y., & Wang, A. (2015). Incorporating Adaptive Local Information into Fuzzy Clustering for Image Segmentation. IEEE Trans. Image Processing, 24(11), 3990-4000.

      [186] Mekhmoukh, A., & Mokrani, K. (2015). Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation. Computer methods and programs in biomedicine, 122(2), 266-281.

      [187] Gong, M., Tian, D., Su, L., & Jiao, L. (2015). An efficient bi-convex fuzzy variational image segmentation method. Information Sciences, 293, 351-369.

      [188] Zhao, X., Li, Y., & Zhao, Q. (2015). Mahalanobis distance based on fuzzy clustering algorithm for image segmentation. Digital Signal Processing, 43, 8-16.

      [189] Al-Ayyoub, M., Abu-Dalo, A. M., Jararweh, Y., Jarrah, M., & Al Sa’d, M. (2015). A gpu-based implementations of the fuzzy c-means algorithms for medical image segmentation. The Journal of Supercomputing, 71(8), 3149-3162.

      [190] Wu, Y., Ma, W., Gong, M., Li, H., & Jiao, L. (2015). Novel fuzzy active contour model with kernel metric for image segmentation. Applied Soft Computing, 34, 301-311.

      [191] Naz, S., Majeed, H., & Irshad, H. (2010, October). Image segmentation using fuzzy clustering: A survey. In International Conference on ICET (pp. 181-186).

      [192] Karmakar, G. C., & Dooley, L. (2001). A generic fuzzy rule based technique for image segmentation. In Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP'01). 2001 IEEE International Conference on (Vol. 3, pp. 1577-1580). IEEE.

      [193] Pednekar, A. S., & Kakadiaris, I. A. (2006). Image segmentation based on fuzzy connectedness using dynamic weights. IEEE Transactions on Image Processing, 15(6), 1555-1562.

      [194] Yaju, L., Baoliang, Z., Li, Z., Dongming, L., Zhenjiang, C., & Lihua, L. (2009, March). Research on image segmentation based on fuzzy theory. In Computer Science and Information Engineering, 2009 WRI World Congress on (Vol. 4, pp. 790-794). IEEE.

      [195] Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.

      [196] Maninis, K. K., Pont-Tuset, J., Arbeláez, P., & Van Gool, L. (2018). Convolutional oriented boundaries: From image segmentation to high-level tasks. IEEE transactions on pattern analysis and machine intelligence, 40(4), 819-833.

      [197] Marmanis, D., Schindler, K., Wegner, J. D., Galliani, S., Datcu, M., & Stilla, U. (2018). Classification with an edge: Improving semantic image segmentation with boundary detection. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 158-172.

      [198] Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., & Kainz, B. (2018). Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE transactions on medical imaging, 37(2), 384-395.

      [199] Skourt, B. A., El Hassani, A., & Majda, A. (2018). Lung CT Image Segmentation Using Deep Neural Networks. Procedia Computer Science, 127, 109-113.

      [200] Baumgartner, C. F., Koch, L. M., Pollefeys, M., & Konukoglu, E. (2017, September). An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation. In International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 111-119). Springer, Cham.

      [201] Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv preprint arXiv:1802.02611.

      [202] Drozdzal, M., Chartrand, G., Vorontsov, E., Shakeri, M., Di Jorio, L., Tang, A., & Kadoury, S. (2018). Learning normalized inputs for iterative estimation in medical image segmentation. Medical image analysis, 44, 1-13.

      [203] Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., & Vercauteren, T. (2018). Interactive medical image segmentation using deep learning with image-specific fine-tuning. IEEE Transactions on Medical Imaging.

      [204] Ranganath, H. S., & Bhatnagar, A. (2018). Image segmentation using two-layer pulse coupled neural network with inhibitory linking field. GSTF Journal on Computing (JoC), 1(2).

      [205] Yang, T., Wu, Y., Zhao, J., & Guan, L. (2018). Semantic segmentation via highly fused convolutional network with multiple soft cost functions. Cognitive Systems Research.

      [206] Feng, H. M., Wong, C. C., Horng, J. H., & Lai, L. Y. (2018). Evolutional RBFNs image model describing-based segmentation system designs. Neurocomputing, 272, 374-385..

      [207] Nie, D., Wang, L., Adeli, E., Lao, C., Lin, W., & Shen, D. (2018). 3-D fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Transactions on Cybernetics.

      [208] Bao, S., & Chung, A. C. (2018). Multi-scale structured CNN with label consistency for brain MR image segmentation. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 6(1), 113-117.

      [209] Roth, H. R., Oda, H., Zhou, X., Shimizu, N., Yang, Y., Hayashi, Y., & Mori, K. (2018). An application of cascaded 3D fully convolutional networks for medical image segmentation. Computerized Medical Imaging and Graphics, 66, 90-99.

      [210] Vardhana, M., Arunkumar, N., Lasrado, S., Abdulhay, E., & Ramirez-Gonzalez, G. (2018). Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cognitive Systems Research, 50, 10-14..

      [211] Yang, Z., Lian, J., Li, S., Guo, Y., Qi, Y., & Ma, Y. (2018). Heterogeneous SPULSE COUPLED NEURAL NETWORK (PCNN) and its application in image segmentation. Neurocomputing, 285, 196-203.

      [212] Zuo, J., Xu, G., Fu, K., Sun, X., & Sun, H. (2018). Aircraft Type Recognition Based on Segmentation With Deep Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters, 15(2), 282-286.

      [213] Fakhry, A., Zeng, T., & Ji, S. (2017). Residual deconvolutional networks for brain electron microscopy image segmentation. IEEE transactions on medical imaging, 36(2), 447-456.

      [214] Xu, Y., Géraud, T., & Bloch, I. (2017, September). From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning. In Image Processing (ICIP), 2017 IEEE International Conference on (pp. 4417-4421). IEEE.

      [215] Wang, Y., Zhu, F., Boushey, C. J., & Delp, E. J. (2017, September). Weakly supervised food image segmentation using class activation maps. In Image Processing (ICIP), 2017 IEEE International Conference on (pp. 1277-1281). IEEE.

      [216] Bi, L., Kim, J., Ahn, E., Kumar, A., Fulham, M., & Feng, D. (2017). Dermoscopic image segmentation via multi-stage fully convolutional networks. IEEE Trans. Biomed. Eng, 64(9), 2065-2074.

      [217] Li, Y., Shen, L., & Yu, S. (2017). HEp-2 specimen image segmentation and classification using very deep fully convolutional network. IEEE transactions on medical imaging, 36(7), 1561-1572.

      [218] Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D. P., & Chen, D. Z. (2017, September). Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 408-416). Springer, Cham.

      [219] Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., & Bengio, Y. (2017, July). The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on (pp. 1175-1183). IEEE.

      [220] Yousif, H., He, Z., & Kays, R. (2017, September). Object segmentation in the deep neural network feature domain from highly cluttered natural scenes. In Image Processing (ICIP), 2017 IEEE International Conference on (pp. 3095-3099). IEEE.

      [221] L. Yang, Y. Zhang, J. Chen, S. Zhang, and D. Z. Chen, “Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation,†2017, pp. 399–407.

      [222] Liew, J., Wei, Y., Xiong, W., Ong, S. H., & Feng, J. (2017, October). Regional interactive image segmentation networks. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2746-2754). IEEE.

      [223] Khened, M., Alex, V., & Krishnamurthi, G. (2017, September). Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image Segmentation and Heart Diagnosis Using Random Forest. In International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 140-151). Springer, Cham.

      [224] P. Moeskops, M. Veta, M. W. Lafarge, K. A. J. Eppenhof, and J. P. W. Pluim, “Adversarial Training and Dilated Convolutions for Brain MRI Segmentation,†2017, pp. 56–64.

      [225] Ravishankar, H., Venkataramani, R., Thiruvenkadam, S., Sudhakar, P., & Vaidya, V. (2017, September). Learning and incorporating shape models for semantic segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 203-211). Springer, Cham.

      [226] Jin, X., Ye, H., Li, L., & Xia, Q. (2017, December). Image Segmentation of Liver CT Based on Fully Convolutional Network. In Computational Intelligence and Design (ISCID), 2017 10th International Symposium on (Vol. 1, pp. 210-213). IEEE.

      [227] Liu, Y., Zhang, Y., & Huang, J. (2017, November). A adaptive segmentation algorithm of ultrasonic image based on simplified PULSE COUPLED NEURAL NETWORK(PCNN). In Intelligent Signal Processing and Communication Systems (ISPACS), 2017 International Symposium on (pp. 784-788). IEEE.

      [228] Salehi, S. S. M., Erdogmus, D., & Gholipour, A. (2017, September). Tversky loss function for image segmentation using 3D fully convolutional deep networks. In International Workshop on Machine Learning in Medical Imaging (pp. 379-387). Springer, Cham.

      [229] Milletari, F., Navab, N., & Ahmadi, S. A. (2016, October). V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 3D Vision (3DV), 2016 Fourth International Conference on (pp. 565-571). IEEE.

      [230] Huang, C., Wu, Q., & Meng, F. (2016, November). QualityNet: Segmentation quality evaluation with deep convolutional networks. In Visual Communications and Image Processing (VCIP), 2016 (pp. 1-4). IEEE.

      [231] Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016, October). 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 424-432). Springer, Cham.

      [232] Nie, D., Wang, L., Gao, Y., & Sken, D. (2016, April). Fully convolutional networks for multi-modality isointense infant brain image segmentation. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 1342-1345). IEEE.

      [233] Moeskops, P., Wolterink, J. M., van der Velden, B. H., Gilhuijs, K. G., Leiner, T., Viergever, M. A., & IÅ¡gum, I. (2016, October). Deep learning for multi-task medical image segmentation in multiple modalities. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 478-486). Springer, Cham.

      [234] Moeskops, P., Viergever, M. A., Mendrik, A. M., de Vries, L. S., Benders, M. J., & IÅ¡gum, I. (2016). Automatic segmentation of MR brain images with a convolutional neural network. IEEE transactions on medical imaging, 35(5), 1252-1261.

      [235] Christ, P. F., Elshaer, M. E. A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., & Sommer, W. H. (2016, October). Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 415-423). Springer, Cham.

      [236] B. Meftah, O. Lézoray, and A. Benyettou, “Novel Approach Using Echo State Networks for Microscopic Cellular Image Segmentation,†Cognit. Comput., vol. 8, no. 2, pp. 237–245, Apr. 2016.

      [237] Fu, H., Xu, Y., Wong, D. W. K., & Liu, J. (2016, April). Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 698-701). IEEE.

      [238] Zhou, D., Zhou, H., Gao, C., & Guo, Y. (2016). Simplified parameters model of PULSE COUPLED NEURAL NETWORK (PCNN) and its application to image segmentation. Pattern Analysis and Applications, 19(4), 939-951.

      [239] Shakeri, M., Tsogkas, S., Ferrante, E., Lippe, S., Kadoury, S., Paragios, N., & Kokkinos, I. (2016). Sub-cortical brain structure segmentation using F-CNN's. arXiv preprint arXiv:1602.02130.

      [240] Xie, W., Li, Y., & Ma, Y. (2016). PULSE COUPLED NEURAL NETWORK (PCNN)-based level set method of automatic mammographic image segmentation. Optik-International Journal for Light and Electron Optics, 127(4), 1644-1650.

      [241] Zhou, X., Ito, T., Takayama, R., Wang, S., Hara, T., & Fujita, H. (2016). Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting. In Deep Learning and Data Labeling for Medical Applications (pp. 111-120). Springer, Cham.

      [242] Liu, N., Li, H., Zhang, M., Liu, J., Sun, Z., & Tan, T. (2016, June). Accurate iris segmentation in non-cooperative environments using fully convolutional networks. In Biometrics (ICB), 2016 International Conference on (pp. 1-8). IEEE.

      [243] Gao, M., Xu, Z., Lu, L., Wu, A., Nogues, I., Summers, R. M., & Mollura, D. J. (2016, April). Segmentation label propagation using deep convolutional neural networks and dense conditional random field. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 1265-1268). IEEE.

      [244] O. Ronneberger, “Invited Talk: U-Net Convolutional Networks for Biomedical Image Segmentation,†2017, pp. 3–3.

      [245] Liu, Z., Li, X., Luo, P., Loy, C. C., & Tang, X. (2015). Semantic image segmentation via deep parsing network. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1377-1385).

      [246] Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015). Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage, 108, 214-224.

      [247] Liu, F., Lin, G., & Shen, C. (2015). CRF learning with CNN features for image segmentation. Pattern Recognition, 48(10), 2983-2992.

      [248] Xu, Y., Jia, Z., Ai, Y., Zhang, F., Lai, M., Eric, I., & Chang, C. (2015, April). Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 947-951). IEEE.

      [249] Duan, Y., Liu, F., Jiao, L., Zhao, P., & Zhang, L. (2017). SAR Image segmentation based on convolutional-wavelet neural network and markov random field. Pattern Recognition, 64, 255-267.

      [250] Q. Ning, J. Zhu, and C. Chen, “Very Fast Semantic Image Segmentation Using Hierarchical Dilation and Feature Refining,†Cognit. Comput., vol. 10, no. 1, pp. 62–72, Feb. 2018.

      [251] Van Der Zwaag, B. J., Slump, C., & Spaanenburg, L. (2002, November). Analysis of neural networks for edge detection. In Proceedings of the ProRISC Workshop on Circuits, Systems and Signal Processing (pp. 580-586).

      [252] Suganthi, D., & Purushothaman, S. (2009). FMRI segmentation using echo state neural network. International Journal of Image Processing, 2(1), 1-9.

      [253] Zhang, X., & Tay, A. L. (2007, August). Fast learning artificial neural network (FLANN) based color image segmentation in RGBSV cluster space. In Neural Networks, 2007. IJCNN 2007. International Joint Conference on (pp. 563-568). IEEE.

      [254] Kazemi, F. M., Akbarzadeh-T, M. R., Rahati, S., & Rajabi, H. (2008, May). Fast image segmentation using C-means based Fuzzy Hopfield neural network. In Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on (pp. 001855-001860). IEEE.

      [255] Yasmin, M., Sharif, M., & Mohsin, S. (2013). Neural networks in medical imaging applications: A survey. World Applied Sciences Journal, 22(1), 85-96.

      [256] Singla, A., & Patra, S. (2017). A fast automatic optimal threshold selection technique for image segmentation. Signal, Image and Video Processing, 11(2), 243-250.

      [257] Ghosh, P., Mitchell, M., Tanyi, J. A., & Hung, A. Y. (2016). Incorporating priors for medical image segmentation using a genetic algorithm. Neurocomputing, 195, 181-194.

      [258] Mesejo, P., Valsecchi, A., Marrakchi-Kacem, L., Cagnoni, S., & Damas, S. (2015). Biomedical image segmentation using geometric deformable models and metaheuristics. Computerized Medical Imaging and Graphics, 43, 167-178.

      [259] Ladgham, A., Hamdaoui, F., Sakly, A., & Mtibaa, A. (2015). Fast MR brain image segmentation based on modified Shuffled Frog Leaping Algorithm. Signal, Image and Video Processing, 9(5), 1113-1120.

  • Downloads

  • How to Cite

    maruthi nagendra prasad, J., & M.VamsiKrishna, D. (2018). A comprehensive study on image segmentation. International Journal of Engineering & Technology, 7(4), 4577-4591. https://doi.org/10.14419/ijet.v7i4.13689