Medical Image Segmentation Techniques, a Literature Review, and Some Novel Trends

Document Type : Original Article

Authors

Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University

Abstract

Segmentation requires the separation or division of an image into regions of similar properties. Image amplitude is the most basic attribute for image segmentation. Image texture and edges are also useful properties for the segmentation process. There is no standard approach for segmentation of an image; no single theory for image segmentation. Segmentation of an image is usually used to mark and determine boundaries and objects (curves, lines, etc.) in an image. More precisely, image segmentation is the process of labeling of every pixel in the image where pixels having the same properties have the same visual properties and share the same group. The result of segmentation process is a number of regions or segments that cover the whole image, or a number of extracted edges and contours of the image. All pixels in the same region are similar according to some characteristics or properties, such as texture, intensity, or color. In this paper a literature review of the various segmentation methods that are available for medical images is presented. Because of image segmentation importance, a set of image segmentation techniques namely; Thresholding techniques, Clustering techniques, Artificial Neural Networks, Edge based techniques, Region based techniques, Watershed, Graph based and Deformable models have been discussed and compared. The features and requirements of several freely and commercial software tools for image segmentation are clarified. The paper is ended by focusing on the novel trends on the topic.

References

Seyed Masoud Nosrati, "Prior Knowledge for Targeted Object Segmentation in
Medical Images", Ph. D thesis, School of Computing Science Faculty of Applied
Sciences, 2015.
[2] SEDA ÇAMALAN, "Analysis of Filtering and Quantization Preprocessing Steps
in Image Segmentation", Ms. C Thesis, Department of Computer Engineering, the
Graduate School of Natural and Applied Science, July 2013.
[3] Bo XIANG, "Knowledge-Based Image Segmentation Using Sparse Shape Priors
and High-Order MRFs", Ph. D thesis, Ecole Centrale de Paris, CVN laboratory,
2013.
[4] Gunnar Läthén,"Segmentation Methods for Medical Image Analysis Blood
vessels, multi-scale filtering and level set methods", Ph. D thesis, Department of
Science and Technology Linköping University, Sweden Norrköping, April 2010.
[5] Mohammad Shajib Khadem, "MRI Brain Image Segmentation Using Graph Cuts",
Ms. C Thesis, Department of Signals and Systems, Chalmers University of
Technology Göteborg, Sweden, 2010.
[6] Mahshid Roumi, "Implementing Texture Feature Extraction Algorithms on
FPGA", Ms. C Thesis, Department of Electrical Engineering, Faculty of Electrical
Engineering, Mathematics and Computer Science, Delft University of Technology,
2009.
[7] Francisco J. Estrada, "Advances in Computational Image Segmentation and
Perceptual Grouping", Ph. D thesis, Department of Computer Science, University
of Toronto, 2005.
[8] http://www.slideshare.net/AboulEllaHassanien/medical-image-analysis-27297012
(Access date, August 2016).
[9] http://www.powershow.com/view1/105a82ZDc1Z/Medical_Image_Segmentation_
powerpoitppt_presentation (Access date, August 2016).
[10] Li Haitao and Li Shengpu, "An Algorithm and Implementation for Image
Segmentation", International Journal of Signal Processing, Image Processing and
Pattern Recognition Vol.9, No. 3, pp. 125-132, 2016.
[11] M. J. Islam, S. Basalamah, M. Ahmadi, and M. A. S. Ahmed, "Capsule Image
Segmentation in Pharmaceutical Applications Using Edge-Based Techniques",
IEEE International Conference on Electro/Information Technology (EIT), pp. 1-5,
2011.
[12] W. Kaihua and B. Tao, "Optimal Threshold Image Segmentation Method Based
on Genetic Algorithm in Wheel Set Online Measurement," in Proc. Third
International Conference on Measuring Technology and Mechatronics Automation
(ICMTMA), pp. 799-802, 2011.
[13] Salem Saleh Al-amri, N.V. Kalyankar and Khamitkar S. D., "Image Segmentation
by Using Threshold Techniques", Journal of Computing, Vol. 2, No. 5, May 2010.
[14] W. Cui and Y. Zhang, "Graph Based Multispectral High Resolution Image
Segmentation", in Proc. International Conference on Multimedia Technology
(ICMT), pp. 1-5, 2010.
[15] F. Zhang, S. Guo, and X. Qian, "Segmentation for Finger Vein Image Based on
PDEs Denoising", in Proc. 3rd International Conference on Biomedical
Engineering and Informatics (BMEI), pp. 531-535, 2010.
[16] A. Xu, L. Wang, S. Feng, and Y. Qu, "Threshold-based level set method of image
segmentation," in Proc. 3rd International Conference on Intelligent Networks and
Intelligent Systems (ICINIS), pp. 703-706, 2010.
[17] S. Zhu, X. Xia, Q. Zhang and K. Belloulata, "An Image Segmentation Algorithm
in Image Processing Based on Threshold Segmentation", in Proc. Third
International IEEE Conference on Signal-Image Technologies and Internet-Based
System, SITIS'0., pp. 673-678, 2007.
[18] F. Jiang, M. R. Frater, and M. Pickering, "Threshold-Based Image Segmentation
Through an Improved Particle Swarm Optimization", in Proc. International
Conference on Digital Image Computing Techniques and Applications (DICTA),
pp. 1-5, 2012.
[19] Nameirakpam Dhanachandra, Khumanthem Manglem and Yambem Jina Chanu, "
Image Segmentation using K-means Clustering Algorithm and Subtractive
Clustering Algorithm", Eleventh International Multi-Conference on Information
Processing, (IMCIP), pp. 764 – 771, 2015.
[20] Alan Jose, S. Ravi and M. Sambath, "Brain Tumor Segmentation using K-means
Clustering and Fuzzy C-means Algorithm and its Area Calculation", In
International Journal of Innovative Research in Computer and Communication
Engineering, Vol. 2, No. 2, March 2014.
[21] Pallavi Purohit and Ritesh Joshi, "A New Efficient Approach Towards K-means
Clustering Algorithm", In International Journal of Computer Applications, Vol.
65, No. 11, March 2013.
[22] S. Kobashi and J. K. Udupa, "Fuzzy Object Model Based Fuzzy Connectedness
Image Segmentation of Newborn Brain MR Images", in Proc. IEEE International
Conference on Systems, Man, and Cybernetics (SMC), pp. 1422-1427, 2012.
[23] A. Fabijanska, "Variance Filter for Edge Detection and Edge-Based Image
Segmentation", in Proc. International Conference on Perspective Technologies and
Methods in MEMS Design (MEMSTECH), pp. 151-154, 2011.
[24] Madhu Yedla, Srinivasa Rao Pathakota and T. M. Srinivasa, "Enhanced K-means
Clustering Algorithm with Improved Initial Center", In International Journal of
Science and Information Technologies, Vol. 1, No 2, pp. 121–125, 2010.
[25] R. Patil and K. Jondhale, "Edge Based Technique to Estimate Number of Clusters
in K-Means Color Image Segmentation", in Proc. 3rd IEEE International
Conference on Computer Science and Information Technology (ICCSIT), pp. 117-
121, 2010.
[26] L. Yucheng and L. Yubin, "An Algorithm of Image Segmentation Based on Fuzzy
Mathematical Morphology", in International Forum on Information Technology
and Applications, IFITA'09, pp. 517-520, 2009.
[27] S. A. Ahmed, S. Dey, and K. K. Sarma, "Image Texture Classification using
Artificial Neural Network (ANN)", in Proc. 2nd National Conference on Emerging
Trends and Applications in Computer Science (NCETACS), pp. 1-4, 2011.
[28] W. Zhao, J. Zhang, P. Li, and Y. Li, "Study of Image Segmentation Algorithm
Based On Textural Features and Neural Network", in International Conference on
Intelligent Computing and Cognitive Informatics (ICICCI), pp. 300-303, 2010.
 
[29] L. Zhang and X. Deng, "The Research of Image Segmentation Based on Improved
Neural Network Algorithm", in Proc. Sixth International Conference on Semantics
Knowledge and Grid (SKG), pp. 395-397, 2010.
[30] D. Barbosa, T. Dietenbeck, J. Schaerer, J. D'hooge, D. Friboulet, and O. Bernard,
"B-Spline Explicit Active Surfaces: An Efficient Framework for Real-Time 3-D
Region-Based Segmentation", IEEE Transactions on Image Processing, Vol. 21,
pp. 241-251, 2012.
[31] T. Mei, C. Zheng, and S. Zhong, "Hierarchical Region Based Markov Random
Field for Image Segmentation", in Proc. International Conference on Remote
Sensing, Environment and Transportation Engineering (RSETE), pp. 381-384,
2011.
[32] Z. Hua, Y. Li, and J. Li, "Image Segmentation Algorithm Based on Improved
Visual Attention Model and Region Growing", in Proc. 6th International
Conference on Wireless Communications Networking and Mobile Computing
(WiCOM), pp. 1-4, 2010.
[33] G. Chen, T. Hu, X. Guo, and X. Meng, "A Fast Region-Based Image
Segmentation Based on Least Square Method", in Proc. IEEE International
Conference on Systems, Man and Cybernetics, SMC, pp. 972-977, 2009.
[34] F. C. Monteiro and A. Campilho, "Watershed Framework to Region-Based Image
Segmentation," in Proc. International Conference on Pattern Recognition, ICPR
19th, pp. 1-4, 2008.
[35] M. R. Khokher, A. Ghafoor, and A. M. Siddiqui, "Image Segmentation using
Fuzzy Rule Based System and Graph Cuts", in Proc. 12th International Conference
on Control Automation Robotics & Vision (ICARCV), pp. 1148-1153, 2012.
[36] Ping Jiang, Quansheng Dou and Xiaoying Hu, "A Parallel Realization of the
Active Contour Model on Boundary Extraction", International Journal of Applied
Mathematics & Information Sciences, Appl. Math. Inf. Sci. Vol. 8, No. 1, pp. 253-
260, 2014.
[37] A. A. Alexandria, P. C. Cortez, J. A. Bessa, J. H. S. Felix, J. S. Abreu and V. H.
Albuquerque, "pSnakes: Anew Radial Active Contour Model and its Application
in the Segmentation of the Left Ventricle from Echocardiographic Images",
Computer Methods and Programs in Biomedicine, Vol. 116, pp. 260-273, 2014.
[38] John A. Bogovic, Jerry L. Prince and Pierre-Louis Bazin,"Multiple-object
Geometric Deformable Model", Computer Vision and Understanding, Vol. 117,
pp. 145-157, 2013.
[39] Zhen Yang, John A. Bogovic, Aaron Carass, Mao Ye, Peter C. Searson, Jerry L.
Prince, "Automatic Cell Segmentation in Fluorescence Images of Confluent Cell
Monolayers Using Multi-Object Geometric Deformable Model", Image
Processing, Medical Imaging, Proc. of SPIE, 2013.
[40] Paresh Chandra Barman et.al. "MRI Image Segmentation Using Level Set Method
and Implement a Medical Diagnosis System", Computer Science & Engineering:
An International Journal (CSEIJ), Vol.1, No.5, December 2011.
[41] J. Xiao, B. Yi, L. Xu, and H. Xie, "An Image Segmentation Algorithm Based on
Level Set Using Discontinue PDE", in Proc. First International Conference on
Intelligent Networks and Intelligent Systems, ICINIS'08, pp. 503-506, 2008.
[42] Kabade Mankarnika Manohar and A. S. Patil, "A Review on Techniques of Image
Segmentation", International Journal of Advanced Research in Computer and
Communication Engineering (IJARCCE), Vol. 5, No. 3, March 2016.

Files

Share

How to cite

Statistics