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Region Merging Strategy Using Statistical Analysis for Interactive Image Segmentation on Dental Panoramic Radiographs

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In low contrast images such as dental panoramic radiographs, the optimum parameters for automatic image segmentation is not easily determined. Semi-automatic image segmentation which is interactively guided by user is one alternative that could provide a good segmentation results. In this paper we proposed a novel strategy of region merging in interactive image segmentation using discriminant analysis on dental panoramic radiographs. A new similarity measurement among regions is introduced. This measurement merges regions which have minimal inter-class variance either with object or background cluster. Since the representative sample regions are selected by user, the similarity between merged regions with the corresponded samples could be preserved. Experimental results show that the proposed region merging strategy give a high segmentation accuracy both for low contrast and natural images.
Copyright © 2017 The Authors - Published by Praise Worthy Prize under the CC BY-NC-ND license.


Dental Panoramic Radiograph; Interactive Image Segmentation; Low Contrast; Region Merging; Statistical Analysis

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J. Ning, L. Zhang, D. Zhang, C. Wu, Interactive image segmentation by maximal similarity based region merging, (2010) Pattern Recognition, 43, pp. 445-456.

G. Chandramohan, S. Subramanian, An Efficient Hybrid Segmentation Algorithm for Computer Tomography Image Segmentation, (2014) International Review on Computers and Software (IRECOS), 9 (9), pp. 1576-1582.

T. N. A. Nguyen, J. Cai, J. Zheng, J. Li, Interactive object segmentation from multi-view images, (2013) Journal of Visual Communication and Image Representation, 24 (4), pp. 477-485.

Y. Attaf, A. Adane, M. Lahdir, A. Boudraa, M. Laghrouche, Z. Ameur, An AM-FM Based Image Segmentation: Detection of Clouds in MSG Images of Algeria, (2015) International Review on Computers and Software (IRECOS), 10 (7), pp. 789-797.

T. Li, Z. Xie, J. Wu, J. Yan, L. Shen, Interactive object extraction by merging regions with k-global maximal similarity, (2013) Neurocomputing, 120, pp. 610-623.

Lakshmi, M., Prasad, S., Rahman, M., Efficient Speckle Noise Reduction Techniques for Synthetic Aperture Radars in Remote Sensing Applications, (2016) International Review of Aerospace Engineering (IREASE), 9 (4), pp. 114-122.

Y. Yang, S. Han, T. Wang, W. Tao, X.-C. Tai, Multilayer graph cuts based unsupervised color-texture image segmentation using multivariate mixed student's t-distribution and regional credibility merging, (2013) Pattern Recognition, 46, pp. 1101-1124.

A. Z. Arifin, A. Asano, Image Segmentation by Histogram Thresholding Using Hierarchical Cluster Analysis, (2006) Pattern Recognition Letters, 27 (13), pp. 1515-1521.

N. Otsu, A threshold selection method from gray-level histograms, (1979) IEEE Transactions of Systems, Man, and Cybernetics, (9), pp. 62-66.

K. McGuinness, N. E. O'Connor, A comparative evaluation of interactive segmentation algorithms, (2010) Pattern Recognition, 43, pp. 434-444.

R. Adams, L. Bischof, Seeded Region Growing,(1994) IEEE Transactions on Pattern Analysis and Machine Intelligence, 16 (6), pp. 641-647.

M. Eapen, R. Korah, Integration of Improved Region Growing (iRG) and Level Set Method for Automated Medical Image Segmentation, (2014) International Review on Computers and Software (IRECOS), 9 (2), pp. 278-284.

M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active Contour Models, (1988) International Journal of Computer Vision, 1, pp. 321-331.

B. Peng, L. Zhang, D. Zhang, J. Yang, Image segmentation by iterated region merging with localized graph cuts, (2011) Pattern Recognition, 44, pp. 2527-2538.

Mo Yan, P.-L. Shui, Interactive Image Segmentation Based on Gaussian Mixture Models with Spatial Prior, (2015) International Journal of Multimedia and Ubiquitous Engineering, 10 (7), pp. 105-114.

E. Zemene, M. Pelillo, Interactive Image Segmentation Using Constrained Dominant Sets, in European Conference on Computer Vision. Springer International Publishing, 2016.

P. Salembier, L. Garrido, Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval, (2000) IEEE Transactions on Image Processing, 9, pp. 561-576.

T. Adamek, Using contour information and segmentation for object registration, modeling and retrieval, Dublin City University: Ph.D. Dissertation, 2006.

X. H. Zeng, R. H. Yi, S. W. Zhu, S. S. He, Auto-marking Image Segmentation Based Manifold Ranking, in International Conference on Artificial Intelligence and Industrial Engineering (AIIE), 2015.

A. S. Sankoh, A. Z. Arifin, A. Y. Wijaya, Extracted Pixels Similarity Features (EPSF) using Interactive Image Segmentation Techniques, (2016) International Journal of Computer Applications, 136 (2), pp. 5-12.

R. S. Basuki, M. Hariadi, E. M. Yuniarno, M. H. Purnomo, Spectral-Based Temporal-Constraint Estimation for Semi-Automatic Video Object Segmentation, (2015) International Review on Computers and Software (IRECOS), 10 (9), pp. 959-965.

N. Shah, N. Bansal, A. Logani, Recent advances in imaging technologies in dentistry, (2014) World Journal of Radiology, 6 (10), pp. 794-807.

S. Geary, F. Selvi, S.-K. Chuang, M. August, Identifying Dental Panoramic Radiograps Features for the Screening of Low Bone Mass in Postmenopausal Women, (2015) International Journal of Oral and Maxillofacial Surgery, 44, pp. 395-399.

K. Horiba, C. Muramatsu, T. Hayashi, T. Fukui, T. Hara, A. Katsumata, H. Fujita, Automated Classification of Mandibular Cortical Bone on Dental Panoramic Radiographs for Early Detection of Osteoporosis, (2015) SPIE Medical Imaging, pp. 94142J-94142J, International Society for Optics and Photonics.

M. S. Kavitha, S.-Y. An, C.-H. An, K.-H. Huh, W.-J. Yi, M.-S. Heo, S.-S. Lee, S.-C. Choi, Texture Analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women, (2015) Oral surgery, oral medicine, oral pathology and oral radiologi, 119 (3), pp. 346-356.

P. H. Shah, R. Venkatesh, Pulp/Tooth Ratio of Mandibular First and Second Molars on Panoramic Radiographs: An Aid for Forensic Age Estimation, (2016) Journal of Forensic Dental Sciences, 8 (2), pp. 112-115.

C, Muramtsu, R. Takahashi, R. Hayashi, T. Hara, T. Fukui, A. Katsumata, H. Fujita, Quantitative Evaluation of Alveolar Bone Resorption on Dental Panoramic Radiographs by Standardized Dentition Image Transformation and Probability Estimation, in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference, pp. 1038-1041, IEEE, 2016.

A. Naik, S. Tikhe, S. Bhide, K. P. Kaliyamurthie, T. Saravanan, Automatic Segmentation of Lower Jaw and Mandibular Bone in Digital Dental Panoramic Radiographs, (2016) Indian Journal of Science and Technology, 9 (11).

"Edison software," [Online]. Available: http://www.caip.

F. S. Kazi, I. A. Rizvi, M. M. Kadam, Segmentation using mean shift algorithm with modifications: A review, (2015) International Journal of Technical Research and Applications, 31, pp. 301-304.

I. Omer, M. Werman, Color lines: image specific color representation, in Computer Vision and Pattern Recognition. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on Vol. 2, 2004.

S. Alpert, M. Galun, R. Basri, A. Brandt, Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration, in IEEE Conference on Computer Vision and Pattern Recognition, 2007.


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