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http://dx.doi.org/10.3745/KIPSTB.2006.13B.4.401

Feature Extraction by Line-clustering Segmentation Method  

Hwang Jae-Ho (한밭대학교 전자공학과)
Publication Information
The KIPS Transactions:PartB / v.13B, no.4, 2006 , pp. 401-408 More about this Journal
Abstract
In this paper, we propose a new class of segmentation technique for feature extraction based on the statistical and regional classification at each vertical or horizontal line of digital image data. Data is processed and clustered at each line, different from the point or space process. They are designed to segment gray-scale sectional images using a horizontal and vertical line process due to their statistical and property differences, and to extract the feature. The techniques presented here show efficient results in case of the gray level overlap and not having threshold image. Such images are also not easy to be segmented by the global or local threshold methods. Line pixels inform us the sectionable data, and can be set according to cluster quality due to the differences of histogram and statistical data. The total segmentation on line clusters can be obtained by adaptive extension onto the horizontal axis. Each processed region has its own pixel value, resulting in feature extraction. The advantage and effectiveness of the line-cluster approach are both shown theoretically and demonstrated through the region-segmental carotid artery medical image processing.
Keywords
Line Cluster; Feature Extraction; Segmentation; Threshold; Carotid Artery Medical Image;
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1 N. Otsu, 'A Threshold Selection Method from Gray-Level Histograms,' IEEE Trans. on Systems, Man, and Cybernetics, Vol.9, No.1, pp.62-66, 1979   DOI   ScienceOn
2 S. Lee and M. M. Crawford, 'Unsupervised multistage image classification using hierarchical clustering with a Bayesian similarity measure,' IEEE Trans. on Image Processing, Vol.14, No.3, pp.312-320, Mar., 2005   DOI   ScienceOn
3 G. J. Mclachlan, Discriminant analysis and statistical pattern recognition, John Wiley, 1992
4 H. Bensmail, G. Celeux, A. E. Raftery and C. P. Robert, 'Inference in Model-Based Cluster Analysis,' Technical Report no. 285, Depart. of Statistics, Univ. of Washington, Mar., 1995
5 F. Forbes and A. E. Raftery, 'Bayesian morphology: Fast unsupervised Bayesian image analysis,' American Statist. Associ., Vol.95, No.446, pp.555-568, Jun. 1999   DOI
6 R. C. Gonzalez and R. E. Woods, Digital image processing, 2'nd ed., Prentice Hall, 2001
7 C. Lu, S. M. Pizer and S. Joshi, 'A Markov random field approach to multi-scale shape analysis,' Proc. of IEEE Conf. on Scale Space Methods in Computer Vision, Isle of Skye, UK, pp.416-431, Jun. 2003
8 박기락 외4, '경동맥 내막 중막 두께와 관상동맥질환의 심한정도와 상관관계.' 순환기학회논문지. Vol.33, No.7, pp.401-408, May. 2003
9 J. Besag, 'On the statistical analysis of dirty pictures,' J. R. Statist. Soc., Vol.48, No.3, pp.259-302, 1986
10 Y. Solihin and C. G. Leedham, 'Interal ratio: A new class of global thresholding techniques for handwriting images,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.21, No.8, pp.761-768, Aug., 1999   DOI   ScienceOn