Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30075
A Review on Image Segmentation Techniques and Performance Measures

Authors: David Libouga Li Gwet, Marius Otesteanu, Ideal Oscar Libouga, Laurent Bitjoka, Gheorghe D. Popa

Abstract:

Image segmentation is a method to extract regions of interest from an image. It remains a fundamental problem in computer vision. The increasing diversity and the complexity of segmentation algorithms have led us firstly, to make a review and classify segmentation techniques, secondly to identify the most used measures of segmentation performance and thirdly, discuss deeply on segmentation philosophy in order to help the choice of adequate segmentation techniques for some applications. To justify the relevance of our analysis, recent algorithms of segmentation are presented through the proposed classification.

Keywords: Classification, image segmentation, measures of performance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2579976

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 361

References:


[1] Gonzalez, R.C., Woods, R.E.: “Digital Imaging Processing”, Prentice Hall: New York, NY, USA, 2002.
[2] Gangwar, S., Chauhan, R.P.: “Survey of Clustering Techniques Enhancing Image Segmentation Process”. International Conference on Advances in Computing and Communication Engineering, 2015, pp. 34-39.
[3] Naz, S., Majeed H., Irshad, H.: “Image segmentation using fuzzy clustering: A survey”, International conference on emerging technologies, 2010, pp 181-186.
[4] Karoui I., Boucher J., Augustin J.: “Variational begion-Based Segmentation Using Multiple Texture Statistics”, IEEE trans on Image processing, 2010, 19, (12), pp. 3146-3156.
[5] Rambabu, C., Chakrabarti, I., Mahanta, A.: “Flooding-based watershed algorithm and its prototype hardware architecture” IEEE Proceedings vision image and signal processing, 2004, 151, (3), pp. 224-234.
[6] Solomon, C.J., Breckon, T.P.: “Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab”, Wiley-Blackwell, 1st edn. 2010.
[7] Tan, K.S., Isa, N.A.M.: “Color image segmentation using histogram thresholding – Fuzzy C-means hybrid approach” Pattern Recognition, 2011, 44, (1), pp. 1–15.
[8] Sahoo, P. K., Soltani, S., Wong, A. K. C.: “A survey of thresholding techniques”, Computer Vision, Graphics, and Image Processing, 1988, 41, (2), pp. 233-260.
[9] Wang, Q., Chang L., Sun Z., Zhou M., Li Q., Liu, H., Guo F.: “An Improved Support Vector Machine Algorithm for Blood Cell Segmentation from Hyperspectral Images”, Institute of Electrical and Electronics Engineers, 2016, pp. 35-39.
[10] Ryu, H., Miyanaga, Y.: “A Study of Image Segmentation Based on a Robust Data Clustering Method”, Electronics and Communications in Japan, 2004, 87, (7), pp. 27-35.
[11] McDonald, J.A., Sheehan, F.H.: “Ventriculogram segmentation using boosted decision trees”, Proc. SPIE 5370, 2004, pp. 1804-1814.
[12] Yoon, H., Han, Y., Hahn, H.: “Image Contrast Enhancement based Sub-histogram Equalization Technique without Over-equalization Noise”, International Journal of Electrical and Electronics Engineering, 2009, 3, (6), pp. 323-329.
[13] Nocedal, J., Wright, S.: “Numerical Optimization”, Springer, 2nd edn. 2006.
[14] Misra, P.R., Si, T.: “Image segmentation using clustering with fireworks algorithm”, Institute of Electrical and Electronics Engineers, 2017, 97-102.
[15] Ashwaq T. Hashim, Duaa A. Noori, An Approach of Noisy Color Iris Segmentation Based on Hybrid Image Processing Techniques, Cyberworlds (CW), International Conference, 2016.
[16] Venketkumar Hariraj, Wan Khairunizam1and Vikneswaran, Zunaidi Ibrahim, Shahriman AB, Zuradzman M. Razlan, Rajendran. T, Sathiyasheelan. R., “Fuzzy Multi-Layer Svm Classification of Breast Cancer Mammogram Images”, International Journal of Mechanical Engineering and Technology, 2018, 9, (8), pp. 1281–1299.
[17] Mike Nachtegael, Dietrich Van der Weken, Dimitri Van De Ville, Etienne E. Kerre “Fuzzy Filters for Image Processing” Springer-Verlag Berlin Heidelberg, 2003.
[18] Songhao Zhu, Xinshuai Zhu, Qingqing Luo, «Graph Theory Based Image Segmentation », 6th International Congress on Image and Signal Processing (CISP), 2013.
[19] Christos H. Papadimitriou and Kenneth Steiglitz, «Combinatorial Optimization: Algorithms and Complexity. » Prentice Hall, Englewood Cliffs, NJ., 1982.
[20] Chudasama, D., Patel, T., Joshi, S.: “Image Segmentation using Morphological Operations”, International Journal of Computer Applications, 2015, 117, (18), pp. 16-19.
[21] Zhang, M., Zhang, L., Cheng, H.D.: “A Neutrosophy approach to image segmentation based on watershed method”, Elsevier Signal Processing, 2010, 90, (5), pp. 1510–1517.
[22] Kass, M., Witkin, A., Terzopoulos, D., “Snakes: Active contour models”, International Journal of Computer Vision, 1988, 1, (4), pp. 321-331.
[23] Lie, J., Lysaker, M., Tai, X.C.: “A variant of the level set method and applications to image segmentation”, Mathematics of Computation, 2006, 75, (255), pp. 1155-1174.
[24] Chan, T.F., Vese, L.A., “Active contours without edges”, IEEE Transactions on Image Processing, 2001, 10, (2), pp. 266–277.
[25] Zhang, R., Huang, Y., Fu, S.: “The development of Markov random field theory and applications on image segmentation algorithm”, International Conference on Communication and Electronics Systems (ICCES), 2016.
[26] Zhang, L., Ji, Q.: “Bayesian Network Model for Automatic and Interactive Image Segmentation » IEEE Transactions on Image Processing, 2011, 20, (9), pp. 2582-2593.
[27] Nock, R., Nielsen, F.: "Statistical Region Merging", IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (11), pp. 1452-1458.
[28] Yu, Y.H., Chang, C.C.: “Scenery image segmentation using support vector machines”, Fundamenta Informaticae, 2004, 61, (3-4), pp. 379–388.
[29] Zhang, H., Fritts, J.E., Goldman, S.A.: “Image segmentation evaluation: A survey of unsupervised methods” Computer Vision and Image Understanding, 2008,110, (2), pp. 260–280.
[30] Celebi, M.E., Lecca, M., Smolka, B.: “Color Image and Video Enhancement”, Springer International Publishing, 2015.
[31] Cardoso, J.S., Corto-real, L.: “Toward a generic evaluation of image segmentation”. IEEE trans. on Image Processing, 2005, 14, (11), pp. 1773-1782.
[32] Vinet L. : “Segmentation et Mise en Correspondance de Régions de Paires d’Images Stéréoscopiques” PhD thesis, Université Paris IX – Dauphine, 1991.
[33] Cocquerez J.P., Philipp-Foliguet, S.: “Analyse d’images: filtrage et segmentation” Masson, Paris, 1995.
[34] Orozco-Monteagudo M., Sahli H., Mihai C., Taboada-Crispi A., “A Hybrid Approach for Pap-Smear Cell Nucleus Extraction”, In: Martínez-Trinidad J.F., Carrasco-Ochoa J.A., Ben-Youssef Brants C., Hancock E.R. (eds) Pattern Recognition. MCPR, 2011, Lecture Notes in Computer Science, vol 6718. Springer, Berlin, Heidelberg.
[35] Yasnoff W. A., Galbraith W., Bacus J. W., “Error measures for objective assessment of scene segmentation algorithms”. AQC, 1979, 1, pp. 107-121.
[36] Rosa, B., Mozer, P., Szewczyk, J.: “An algorithm for calculi segmentation on ureteroscopic images”. International Journal of Computer Assisted Radiology and Surgery, 2011, 6, (2), pp 237–246.
[37] Migniot, C., Bertolino, P., Chassery, J.M.: “Automatic people segmentation with a template-driven graph cut” IEEE International Conference on Image Processing (ICIP), 2011.
[38] Martin, D.R.: “An Empirical Approach to Grouping and Segmentation”, PhD Thesis, University of California, Berkeley, 2002.
[39] Vojodi., H., Moghadam., A.M.E.: «A Supervised Evaluation Method Based on Region Shape Descriptor for image Segmentation Algorithm », The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), 2012.
[40] Iancu, A., Popescu, B., Brezovan, M., Ganea, E.: “Quantitative Evaluation of Color Image Segmentation Algorithms ». International Journal of Computer Science and Applications, 2011, 8, (1), pp. 36-53.
[41] Powers, D.M.W.: “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation", Journal of Machine Learning Technologies, 2011, 2, (1), pp. 37–63.
[42] Fawcett, T.: “An Introduction to ROC Analysis”, Pattern Recognition Letters, 2006, 27, (8), pp. 861–874.
[43] Dubuisson, M.P., Jain, A.K.: “A modified Hausdorff distance for object matching.” Conference on pattern recognition, 1994, pp 566–568.
[44] Arbabshirani, M.R., Dallal, A.H., Agarwal, C., Patel, A., Moore, G.: “Accurate Segmentation of Lung Fields on Chest Radiographs using Deep Convolutional Networks”, Proc. of SPIE, 2017.
[45] Tareef, A., Song, Y., Cai, W., Wang, Y., Feng, D.D, Mei Chen, M.: “Automatic nuclei and cytoplasm segmentation of leukocytes with color and texture-based image enhancement”, Biomedical Imaging (ISBI), IEEE 13th International Symposium, 2016, pp. 935 - 938.
[46] Weszka, J.S., Rosenfeld, A.: “Threshold evaluation techniques”, IEEE Transactions on Systems, Man and Cybernetics, 1978, 8, (8), pp. 622– 629.
[47] Otsu N.: “A threshold selection method from gray-level histograms”, IEEE Transactions on Systems, Man and Cybernetics, 1979, 9, (1), pp. 62– 66.
[48] Levine, M.D., Nazif, A.M.: “Dynamic Measurement of Computer Generated Image Segmentations”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985, 7, (2), pp. 155–164.
[49] Liu, J., Yang, Y.H.: “Multi-resolution color image segmentation”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 7, (2), pp. 689–700.
[50] Borsotti, M., Campadelli, P., Schettini, R.: “Quantitative evaluation of color image segmentation results”, Pattern Recognition Letters, 1998, 19, (8), pp. 741–747.
[51] Rosenberger, C., Chehdi, K., “Genetic fusion: application to multicomponent image segmentation”, Proceedings of ICASSP-4 Istanbul, Turkey, 2000.
[52] Chabrier, S., Emile, B., Laurent, H., Rosenberger, C., Marche, P.: “Unsupervised evaluation of image segmentation application to multispectral images”. Proceedings of the 17th international conference on pattern recognition, 2004.
[53] Chen, H.C., Wang, S.J.: “The use of visible color difference in the quantitative evaluation of color image segmentation”, Proceedings of ICASSP, 2004.
[54] Guigues, L., Men, H.L., Cocquerez, J.P.: “The hierarchy of the cocoons of a graph and its application to image segmentation”, Pattern Recognition Letters, 2003, 24, (3), pp. 1024-1066.
[55] Handl, J., Knowles, K., Kell, D.: “Computational cluster validation in post-genomic data analysis”, Bioinformatics, 2005, 21, (15), pp. 3201-3212.
[56] Dunn, J.C., “Well-separated clusters and optimal fuzzy partitions”, Journal of cybernetics, 1974, 4, (1), pp. 95–104.
[57] Priya Ranjan; Si, Tapas, “Image segmentation using clustering with fireworks algorithm”, Institute of Electrical and Electronics Engineers, 2017, pp. 97-102.
[58] Chaoen Hu, Xia Liu, Xiao Liang, Hui, Xin Yang, Jie Tian, “Brain vascular image Segmentation Based on Fuzzy Local Information C-Means Clustering”, Imaging Manipulation, and Analysis of Biomolecules, Cells, and Tissues XV, Proc. of SPIE, 2017.
[59] Mohammad R. Arbabshirani, Ahmed H. Dallal, Chirag Agarwal, Aalpen Patel, and Gregory Moore, “Accurate Segmentation of Lung Fields on Chest Radiographs using Deep Convolutional Networks”, Proc. of SPIE, 2017.
[60] Henry A. Leopold, Jeff Orchard, John Zelek, and Vasudevan Lakshminarayanan, “Segmentation and Feature Extraction of Retinal Vascular Morphology”, Proc of SPIE, 2017.
[61] David Joon Ho; Paul Salama; Kenneth W. Dunn; Edward J. Delp “Boundary segmentation for fluorescence microscopy using steerable filters”. SPIE - International Society for Optical Engineering, 2017.
[62] Alban Ngatchou, Laurent Bitjoka, Etienne Mfoumou, Ousman Boukar, Mitherand Ngatcheu and Martin Ngueguim, “Robust and Fast Segmentation Based on Fuzzy Clustering Combined with Unsupervised Histogram Analysis”, IEEE Intelligent Systems, 2017, October, pp 1-9.
[63] Ashwaq T. Hashim, Duaa A. Noori, “An Approach of Noisy Color Iris Segmentation Based on Hybrid Image Processing Techniques”, Cyberworlds (CW), International Conference,2016.
[64] Eva Tuba, Lazar Mrkela, Milan Tuba, “Retinal Blood Vessel Segmentation by Support Vector Machine Classification”, Institute of Electrical and Electronics Engineers, 2017, pp 1-6.
[65] Amir Razi, Wei-Wei Wang, Xiang-Chu Feng, « Image Segmentation by Active Contour Model with a New Data Fidelity », International Conference on Machine Vision and Information Technology, 2017.
[66] Chieh-Ling Huang, Jwu-Jenq Chen, Ching-Ju Chen, Yung-Gi Wu, “Geological segmentation on UAV aerial image using shape-based LSM with dominant color”, 30th International Conference on Advanced Information Networking and Applications Workshops, 2016.
[67] Ganesan P, B.S. Sathish, G. Sajiv, “Automatic Segmentation of Fruits in CIELuv Color Space Image using Hill Climbing Optimization and Fuzzy C-Means Clustering”, world Conference on Futuristic trends in Research and innovation for Social Welfare, 2016.
[68] Niyas S, Reshma P and Sabu M Thampi, “A Color Image Segmentation Scheme for Extracting Foreground from Images with Unconstrained Lighting Conditions”, Springer International Publishing, 2016.
[69] Qian Wang, Li Chang, Zhen Sun, Mei Zhou, Qingli Li, Hongying Liu, Fangmin Guo, “An Improved Support Vector Machine Algorithm for Blood Cell Segmentation From Hyperspectral Images”, Institute of Electrical and Electronics Engineers, 2016, pp 35-39.
[70] Amiya Halder, Anuva Pradhan, Sourjya Kumar Dutta and Pritam Bhattacharya, “Tumor Extraction from MRI images using Dynamic Genetic Algorithm based Image Segmentation and Morphological Operation”, International Conference on Communication and Signal Processing, 2016, April 6-8.
[71] Berthon, Beatrice, Marshall, Christopher., Evans, Mererid and Spezi, Emiliano, “ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography”, Physics in Medicine and Biology, 2016, 61 (13), pp. 4855-4869.
[72] Bo Chenab, Qing-Hua Zouab, Wen-Sheng Chenab & Bin-Bin Panab, « A novel adaptive partial differential equation model for image segmentation », Applicable Analysis, 2014, Vol. 93, No. 11, 2440–2450
[73] Chen Hejun, Ding Haiqiang, He Xiongxiong, Zhuang Hualiang, « color image segmentation based on seeded region growing with canny edge detection », Signal Processing (ICSP),12th International Conference, 2014.
[74] Fang Yang, Xiaomei Wang, «Automatic Segmentation of Bladder Layers in Optical Coherence Tomography Images Using Graph Theory and Dynamic Programming », 6th International Congress on Image and Signal Processing (CISP), 2013.
[75] Ling Zhang, Ming Zhang Heng-Da Cheng, “Color image segmentation based on Neutrosophy”, Optical Engineering, 2012, 51.
[76] Ming Zhang, Ling Zhang, H.D. Cheng, “A neutrosophic approach to image segmentation based on watershed method”, Elsevier Signal Processing,2010, 90: 1510–1517
[77] Grau V., A. U. J. Mewes, M. Alcañiz, R. Kikinis, and S. K. Warfield, “Improved Watershed Transform for Medical Image Segmentation Using Prior Information”, IEEE transactions on medical imaging, 2004, 23.
[78] Celebi, M.E., Schaefer, G., Iyatomi, H., Stoecker, W.V.: “Lession border detection in dermoscopy images”, Comput Med Imag Graph 2009, 33, (2), pp. 148–153.
[79] Varshney, S.S., Rajpal, N., Purwar, R.: “Comparative study of image segmentation techniques and object matching using segmentation”, in 2009 Proceeding of International Conference on Methods and Models in Computer Science (ICM2CS).