Improving Age Estimation of Dental Panoramic Images Based on Image Contrast Correction by Spatial Entropy Method
Subject Areas : electrical and computer engineeringMasoume Mohseni 1 , Hussain Montazery Kordy 2 , Mehdi Ezoji 3
1 -
2 - Babol Noshirvani University of Technology
3 - Babol Noshivani University of Technology
Keywords: Image resolution enhancement, dental segmentation, image processing, age estimation, dental radiography,
Abstract :
In forensic dentistry, age is estimated using dental radiographs. Our goal is to automate these steps using image processing and pattern recognition techniques. With a dental radiograph, the contour is extracted and features such as apex, width and tooth length are determined, which are used to estimate age. Optimizing the resolution of radiographic images is an important step in contour extraction and age estimation. In this article, the aim is to improve the image resolution in order to extract the appropriate area and proper segmentation of the tooth, which makes it possible to estimate age better. In this model, due to the low resolution of radiographic images, in order to increase the accuracy of extracting the desired area of each tooth (ROI), the image resolution increases using spatial entropy based on the spatial distribution of pixel brightness, along with another increasing resolution method, like the Laplacian pyramids. Increasing the resolution of the image leads to the extraction of appropriate ROI and the removal of unwanted areas. The database used in this study is 154 adolescent panoramic radiographs, of which 73 are male and 81 are female. This database is prepared from Babol University of Medical Sciences. The results show that by using fixed tooth segmentation methods and only by applying the proposed effective method to improve image resolution, the extraction of appropriate ROI increased from 66% to 78% which shows a good improvement. The extracted ROI is then delivered to the segmented block and the contour extracted. After contour extraction, age is estimated. The age estimation using the proposed method is closer to the manual age estimate compared to the method that does not use the proposed algorithm to increase the image resolution.
[1] A. S. Panchbhai, "Dental radiographic indicators, a key to age estimation," Dentomaxillofacial Radiology, vol. 40, no. 4, pp. 199-212, May 2011.
[2] R. Cameriere, L. Ferrante, and M. Cingolani, "Age estimation in children by measurement of open apices in teeth," Int. J. Legal. Med., vol. 120, no. 1, pp. 49-52, Jan. 2006.
[3] A. Lurie, G. M. Tosoni, J. Tsimikas, and W. Fitz, "Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images," Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, vol. 113, no. 4, pp. 549-558, Apr. 2012.
[4] D. Bruellmann, S. Sander, and I. Schmidtmann, "The design of a fast fourier filter for enhancing diagnostically relevant structures-endodontic files," Computers in Biology and Medicine, vol. 72, pp. 212-217, May 2016.
[5] Y. Y. Amer and M. J. Aqel, "An efficient segmentation algorithm for panoramic dental images," Procedia Computer Science, vol. 65, pp. 718-725, 2015.
[6] S. Tikhe, A. Naik, S. Bhide, T. Saravanan, and K. Kaliyamurthie, "Algorithm to identify enamel caries and interproximal caries using dental digital radiographs," in Proc. Int. Advanced Computing Conf., IACC’16, pp. 225-228, Bhimavaram, India, 27-28 Feb. 2016.
[7] M. M. Hasan, R. Hassan, and W. Ismail, "Automatic segmentation of jaw from panoramic dental x-ray images using GVF snakes," in Proc. World Automation Congress, WAC’16, 6 pp., Rio Grande, PR, USA, 31 Jul.-4 Aug. 2016.
[8] R. B. Ali, R. Ejbali, and M. Zaied, "GPU-based segmentation of dental x-ray images using active contours without," in Proc. Int. Conf. on Intelligent Systems Design and Applications, pp. 505-510, Marrakech, Morocco, 14-16 Dec. 2015.
[9] J. Kaur and J. Kaur, "Dental image disease analysis using pso and backpropagation neural network classifier," International J. of Advanced Research in Computer Science and Software Engineering, vol. 6, no. 4, pp. 158-160, Apr. 2016.
[10] M. K. Alsmadi, "A hybrid fuzzy c-means and neutrosophic for jaw lesions segmentation," Ain Shams Engineering J., vol. 9, no. 4, pp. 697-706, Dec. 2015.
[11] L. H. Son and T. M. Tuan, "A cooperative semi-supervised fuzzy clustering framework for dental x-ray image segmentation," Expert Systems with Applications, vol. 46, no. C, pp. 380-393, Mar. 2016.
[12] A. K. Jain and H. Chen, "Matching of dental x-ray images for human identification," Pattern Recognition vol. 37, no. 7, pp. 1519-1532, Jul. 2004.
[13] O. Nomir and M. Abdel-Mottaleb, "A system for human identification from x-ray dental radiographs," Pattern Recognition vol. 38, no. 8, pp. 1295-1305, Aug. 2005.
[14] E. H. Said, D. E. M. Nassar, G. Fahmy, and H. H. Ammar, "Teeth segmentation in digitized dental x-ray films using mathematical morphology," IEEE Trans. Inf. Forensic Secur., vol. 1, no. 2, pp. 178-189, Jun. 2006.
[15] N. Al-sherif, G. Gue, and H. H. Ammar, "A new approach to teeth segmentation," IEEE Int. Sympo. on Multimedia, pp. 145-148, Irvine, CA, USA, 10-12 Dec. 2012.
[16] P. L. Lin, Y. H. Lai, and P. W. Huang, "An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information," Pattern Recognition, vol. 43, no. 4, pp. 1380-1392, Apr. 2010.
[17] S. Shah, A. Ross, and H. H. Ammar, "Automatic teeth segmentation using active contour without edges," in Proc. of the Biometrics Symp.: Special Session on Research at the Biometric Consortium Conf., pp. 145-148, Baltimore, MD, USA, 19 Sept.-21 Aug. 2006.
[18] J. Zhou and M. Abdel-Mottaleb, "A content-based system for human identification based on bitewing dental x-ray images," Pattern Recognition, vol. 38, no. 11, pp. 2132-2142, Nov. 2005.
[19] M. A. Mahoor and M. Abdel-Mottaleb, "Classification and numbering of teeth in dental bitewing images," Pattern Recognition, vol. 38, no. 4, pp. 577-586, Apr. 2005.
[20] Y. H. Lai and P. L. Lin, "Effective segmentation for dental x-ray images using texture-based fuzzy inference system," in Proc. 10th Int. Conf. on, Advanced Concepts for Intelligent Vision Systems, pp. 936-947, 20-24 Oct. 2008.
[21] F. Keshtkar and G. Gueaieb, "Segmentation of dental radiographs using a swarm intelligence approach," in Proc. IEEE Canadian Conf. on Electrical and Computer Engineering, pp. 328-331, Ottawa, Canada, 7-10 May 2006.
[22] P. L. Lin, Y. H. Lai, and P. W. Huang, "Dental biometrics: human identification based on teeth and dental works in bitewing radiographs," Pattern Recognition, vol. 45, no. 3, pp. 934-946, Mar. 2012.
[23] P. Choorat, W. Chiracharit, and K. Chamnongthai, "A single tooth segmentation using structural orientations and statistical textures," in Proc. the Biomedical Engineering Int. Conf., pp. 294-297, Chiang Mai, Thailand, 29-31 Jan.. 2011.
[24] P. L. Lin, P. W. Huang, Y. S. Cho, and C. H. Kuo, "An automatic and effective tooth isolation method for dental radiographs," Opto-Electronics Review, vol. 21, pp. 126-136, 2013.
[25] P. L. Lin, P. Y. Huang, P. W. Huang, H. C. Hsu, and C. C. Chen, "Teeth segmentation of dental priapical radiographs based on local singularity analysis," Computer Methods and Programs in Biomedicine, vol. 113, no. 2, pp. 433-445, Feb. 2014.
[26] L. Hoang and T. Manh, "Dental segmentation from x-ray images using semi-supervised fuzzy clustering with spatial constraints," Engineering Application of Artificial Intelligence, vol. 59, no. C, pp. 186-195, 2017.
[27] Y. Gan, et al., "Tooth and alveolar bone segmentation from dental computed tomography image," IEEE J. of Biomedical and Health Information, vol. 22, no. 1, pp. 196-204, Jan. 2018.
[28] G. Silva, L. Oliveira, and M. Pithon, "Automatic segmentation teeth in x-ray images: trends, set, benchmarking and future perspective," Expert Systems with Applications, vol. 107, pp. 15-31, 1 Oct. 2018.
[29] T. Celik, "Spatial entropy-based global and local image contrast enhancement," IEEE Trans. on Image Processing, vol. 23, no. 12, pp. 5298-5308, Dec. 2014.
[30] D. Frejlichowsky and R. Wanat, "Application of the laplacian pyramid decomposition to the enhancement of digital dental radiographic images for the automatic person identification," A. Campilho and M. Kamel, (eds.) Image Analysis and Recognition: 7th Int. Conf., ICIAR 2010, Part II. LNCS, vol. 6112, pp. 151-160, Springer, Heidelberg 2010.
[31] ش. جوادینژاد، م. مهدیزاده و ر. ترابی، "تعیین دقت روش کمریر (Cameriere) در تعیین سن تقویمی،" مجله دانشکده دندانپزشکی اصفهان، جلد 8، شماره 4، صص. 314-321، مهر 1391.
[32] C. Giardina and E. Doughetr, Morphological Methods in Image and Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, 1988.
[33] R. Wanat and D. Frejlichowsky, "A problem of automatic segmentation of digital dental panoramic x-ray images for forensic human identification," in Proc. 16th Int. Conf., pp. 294-302, Ravenna, Italy, Sept. 2011.
[34] N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst. Man Cybern., vol. 9, no. 1, pp. 62-66, Jan. 1979.