Abstract
Image segmentation has played an important role in computer vision especially for human tracking. The result of image segmentation is a set of segments that collectively cover the entire image or a set of contours extracted from the image. Its accuracy but very elusive is very crucial in areas as medical, remote sensing and image retrieval where it may contribute to save, sustain and protect human life. This paper presents the analysis and implementation using MATLAB features and one best result can be selected for any algorithm using the subjective evaluation. We considered the techniques under the following five groups: Edge-based, Clustering-based, Region-based, Threshold-based and Graph-based.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gonzalez RC, Woods RE (2002) Digital image processing. 2nd Prentice-Hall Inc, Upper Saddle River
Shapiro LG, Stockman GC (2001) Computer vision. Prentice-Hall Inc., Upper Saddle River, pp 279–325
Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Underst 10(2):260–280
Polak M, Zhang H, Pi M (2009) An evaluation metric for image segmentation of multiple objects. Image Vis Comput 27(8):1223–1227
Hu S, Hoffman EA, Reinhardt JM (2001) Automatic lung segmentation for accurate quantization of volumetric X-ray CT images. IEEE 20(6):490–498
Zhang YJ (2001) A review of recent evaluation methods for image segmentation. Paper presented at the International Symposium on Signal Processing and its Applications (ISSPA), Kuala Lumpur
Udupa JK, Leblanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM et al (2006) A framework for evaluating image segmentation algorithms Comput Med Imaging Graph 30:75–87
Varshney SS, Rajpal N, Purwar R (2009) Comparative study of image segmentation techniques and object matching using segmentation. Paper presented at the international conference on methods and models in computer science
Wang L, He L, Mishra A, Li C (2012) Active contours driven by local Gaussian distribution fitting energy. Signal Process 2(3):737–739
Wang Y, Guo Q, Zhu Y (2007) Medical image segmentation based on deformable models and its applications. Springer, p 2
Boucheron LE, Harvey NR, Manjunath BS (2007) A quantitative object-level metric for segmentation performance and its application to cell nuclei. Springer, pp 208–219
Padmavathi G, Subashini P, Sumi A (2010) Empirical evaluation of suitable segmentation algorithms for IR images. IJCSI Int J Comput Sci 7(4)(2):
Mobahi H, Rao SR, Yang AY, Sastry SS, Ma Y. Segmentation of natural images by texture and boundary compression
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this paper
Cite this paper
Verma, S., Khare, D., Gupta, R., Chandel, G.S. (2013). Analysis of Image Segmentation Algorithms Using MATLAB. In: Das, V. (eds) Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing. Lecture Notes in Electrical Engineering, vol 150. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3363-7_19
Download citation
DOI: https://doi.org/10.1007/978-1-4614-3363-7_19
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-3362-0
Online ISBN: 978-1-4614-3363-7
eBook Packages: EngineeringEngineering (R0)