Back to Current Issues

Texture Image Segmentation Based on threshold Techniques

Dodla. Likhith Reddy, Dr. D Prathyusha Reddi,

Professor, Dept.of ECE, PBR VITS, KAVALI
:10.22362/ijcert/2017/v4/i3/xxxx [UNDER PROCESS]

Image segmentation is the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is used to give the values of objects and boundaries of a selected image like lines, curves. The image segmentation plays a critical role in a variety of pattern recognition applications such as robot vision, cartography, criminal investigation, remote sensing, object identification and recognition, military surveillance, quality assurance in industries, facial recognition and medical imaging, etc. The main aim of this paper is to propose methods are improving image segmentation and give the clear object about the image by using different techniques. This article presents a brief outline of some of the most commonly used segmentation techniques like Thresholding, Region based and Edge detection methods. The proposed methods implemented in MATLAB.

Dodla. Likhith Reddy, “Texture Image Segmentation Based on threshold Techniques”, International Journal Of Computer Engineering In Research Trends, 4(3):69-75, March-2017.

Keywords : Segmentation, Edge Detection, Region Based, threshold-based segmentation techniques.

[1]	A. Bovik, M. Clark, W.S. Geisler, Multichannel texture analysis using localized spatial filters, IEEE Trans. Pattern Anal. and Machine Intelligence 12 (1990), 55-73
[2]	A. Khotanzad, A. Bouarfa, A parallel non-parametric clustering algorithm with application to image segmentation, Proc. 22nd Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA,( 1988)305-309
[3]	A. Laine, J. Fan, Frame representations for texture segmentation, IEEE Trans. Image Processing,  5 (1996) 771-780
[4]	A.K. Jain, F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, 24 (1991) 1167-1186
[5]	A.K. Jain, K. Karu, Learning texture discrimination masks, IEEE Trans. Pattern Anal. Machine Intelligence 18 (1996) 195-205.
[6]	Ahmed R. Khalifa et al.,Evaluating The Effectiveness Of Region Growing And Edge Detection Segmentation Algorithms,. Journal of American Science,6(10), (2010) , 580-587
[7]	Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: ‘Contour detection and hierarchical image segmentation’, IEEE Trans. Patt. Anal. Mach. Intell., 33, (5), (2010) 898–916
[8]	Arifin, A.Z., Asano, A.: ‘Image segmentation by histogram thresholding using hierarchical cluster analysis’, Patt. Recogn.Lett., 27, (13), (2006)1515–1521
[9]	AshwiniKunte, Anjali Bhalchandra, Efficient DIS Based Region Growing Segmentation Technique for VHR Satellite Images , ICGST-GVIP Journal, Volume 10, Issue 3, (2010)
[10]	B.B. Chaudhuri, N. Sarkar, Texture segmentation using fractal dimension, IEEE Trans. Pattern Anal. Machine Intelligence, 17 (1995) 72-77
[11]	B.S. Manjunath, R. Chellappa, Unsupervised texture segmentation using Markov random field models, IEEE Trans. on Pattern Anal. Machine Intelligence 13 (1991) 478-482
[12]	C. Bouman, B. Liu, Multiple resolution segmentation of textured images, IEEE Trans. Pattern Anal. Machine Intelligence 13 (1991) 99-113
[13]	Canny, J.: ‘A computational approach to edge detection’, IEEE Trans. Patt. Anal. Mach. Intell., 8, (6), (1986) 679–698
[14]	Carreira-Perpinan, M.A.: Acceleration strategies for Gaussian mean-shift image segmentation. Proc. IEEE Conf. on Computer Vision Pattern Recognition,  vol. 1, (2006)1160–1167
[15]	Carreira-Perpinan, M.A.: Acceleration strategies for Gaussian mean-shift image segmentation. Proc. IEEE Conf. on Computer Vision Pattern Recognition,  vol. 1, (2006) 1160–1167
[16]	Chen, T.W., Chen, Y.L., Chien, S.Y.: Fast image segmentation based on K-means clustering with histograms in HSV color space. Proc. IEEE Int. Workshop on Multimedia Signal Processing, October (2008) 322–325
[17]	Cheng, Y.: ‘Mean shift, mode seeking, and clustering’, IEEE Trans.Patt. Anal. Mach. Intell., 1995, 17, (8),  (1995) 790–799
[18]	Comaniciu, D., Meer, P.: ‘Mean shift: a robust approach toward feature space analysis’, IEEE Trans. Patt. Anal. Mach. Intell., 24, (5),  (2002) 603–619
[19]	Cour, T., Benezit, F., Shi, J.: ‘‘Spectral segmentation with Multiscale graph decomposition’‘. Proc. IEEE Conf. on Computer Vision Pattern Recognition, vol. 2, (2005) 1124–1131
[20]	Cui.Y, Dong.H, Zhou.E.Z, “An Early Fire Detection Method Based on Smoke Texture Analysis and Discrimination”, Journal Congress on Image and Sig. Proc., (2008), 95–99.
[21]	Cula.O.G and Dana.K.J, 3D texture recognition using bidirectional feature histograms, in Int. Journal Comp. Vis., vol.59, (2004 )33–60
[22]	D.K. Panjwani, G. Healey, “Markov random field models for unsupervised segmentation of textured color images”, IEEE Trans. Pattern Anal. Machine Intelligence, 17 (1995), 939-954
[23]	Delon, J., Desolneux, A., Lisani, J.L., Petro, A.B.: ‘A nonparametric approach for histogram segmentation’, IEEE Trans. Image Process., 16, (1),  (2007) 253–261.
[24]	Donald.A, Adjeroh and UmasankarKandaswamy, “Texton-based segmentation of retinal vessels”, Journal of Optical Society of America , vol. 24, no. 5,(2007)  1384–1393
[25]	Duda, R.O., Hart, P.E.: ‘Pattern classification and scene analysis’, (Wiley, 1973).
[26]	F.S. Cohen, Z. Fan, Maximum likelihood unsupervised textured image segmentation, CVGIP: Graphical Models and Image Processing 54 (1992) 239-251
[27]	Ghamisi, P., Couceiro, M.S., Benediktsson, J.A., Ferreira, N.M.: ‘‘An efficient method for segmentation of images based on fractional calculus and natural selection’‘, Expert Syst. 39, (16), (2012)12407–12417.
[28]	Glasbey, C.A.: ‘An analysis of histogram-based thresholding algorithms’, Comput. Vis. Graph. Image Process., 55, (6),  (1993) 532–537
[29]	H. D. Cheng and Y. Sun, “A hierarchical approach to color image segmentation using homogeneity”, IEEE Transaction on Image Processing, vol. 9, no. 12, 2000 (2071- 2082)
[30]	H. Greenspan, R. Goodman, R. Chellappa, C.H. Anderson, “Learning texture discrimination rules in a multiresolution system”, IEEE Trans. Pattern Anal. Machine Intelligence 16 (1994) 894-901
[31]	H. Seddik and E. Ben Braiek “Color Medical Images Watermarking, Based Neural Network Segmentation “GVIP Journal Special Special Issue on (Medical Image Processing), (2006) 81-86
[35]	Huang, S.H., Chu, Y.H., Lai, S.H., Novak, C.L.: ‘‘Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI’‘, IEEE Trans. Med. Imag., 28, (8), (2009) 1595–1605
[36]	Idrissisidiyassine, Samir belfkih, "Texture image segmentation using a new descriptor and mathematical morphology”, in Int. Arab Journal of Information Technology, Vol.10, No.2, (2013)  204-208
[37]	J. Mao, A.K. Jain, “Texture classification and segmentation using multi resolution simultaneous autoregressive models”, Pattern Recognition 25 (1992) 173-188
[38]	. Serra, Image Analysis and Mathematical Morphology. London, U.K.: Academic, 1982
[39]	J.F. Silverman, D.B. Cooper, Bayesian clustering for un-supervised estimation of surface and texture models, IEEE Trans. Pattern Anal. Machine Intelligence 10 (1988) 482-495
[40]	J.H.Jaseema Yasmin1, D. Muhammad Noorul Mubarak2 , M.MohamedSathik 3, Border Detection of Noisy Skin Lesions by Improved Iterative Se]gmentation Algorithm using LOG Edge Detector ,  ICGST-GVIP Journal, Vol. 12 ( 2),  (2012) 56-64
[41]	J.L. Chen, A. Kundu, Unsupervised texture segmentation using multichannel decomposition and hidden Markov models, IEEE Trans. Image Processing, 4 (1995), 603-619
[42]	.Y. Hsiao, A.A. Sawchuk, “Unsupervised texture image segmentation using feature smoothing and probabilistic relaxation techniques”, Computer Vision Graphics Image Processing 48 (1989) 1-21
[43]	Jähne, B.: ‘Practical handbook on image processing for scientific and technical applications’ (CRC Press, 2004, 2nd Ed.), Ch. 15
[44]	Kekre.H.B,  SayleeGharge, "Texture Based Segmentation using Statistical Properties for Mammographic Images”, Int. Journal of Advanced Computer Science and Applications(IJACSA),  Vol. 1, No. 5, (2010) 102-107
[45]	Otsu, N.: ‘A threshold selection method from gray-level histograms’, IEEE Trans. Syst., Man, Cybern., 9, (1), (1979) 62–66. 

DOI Link : Not yet assigned

Download :

Refbacks : There are currently no refbacks

Quick Links


Science Central

Score: 13.30

Submit your paper to