Teeth Segmentation of Bitewing X-Ray Images Using Wavelet Transform

被引:3
作者
Salimzadeh, Sina [1 ]
Kandulu, Sara [2 ]
机构
[1] Girne Amer Univ, Dept Elect & Elect Engn, Mersin 10, Kyrenia, Turkey
[2] Girne Amer Univ, Fac Engn Technopk Bldg,Univ Dr,Karmi Campus, Karaoglanoglu, Turkey
来源
INFORMATICA-AN INTERNATIONAL JOURNAL OF COMPUTING AND INFORMATICS | 2020年 / 44卷 / 04期
关键词
teeth segmentation; bitewing X-ray images; dental radiographs enhancement; wavelet transform; morphological operations; HUMAN IDENTIFICATION; SYSTEM; CLASSIFICATION;
D O I
10.31449/inf.v44i4.2774
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Within the recent twenty years, the dental X-ray images have widely been employed in forensic odontology for human identification, particularly where mass disasters happen. In this paper, a novel method is proposed for the process of teeth segmentation and individual teeth isolation of Bitewing X-ray radiographs. The main objective of this study is to develop an automatic teeth segmentation approach that can be used in an Automated Dental Identification System (ADIS). The proposed method is based on separating teeth according to edge lines between crowns of teeth. It comprises four phases as image enhancement, edge detection by using wavelet transform, Region of Interest (ROI) definition, and morphological processing. Image enhancement in our case is done by image sharpening using a Butterworth high pass filter. Directional changes of the image and a blurred version of it are obtained by wavelet transform in the second phase. In ROI definition the upper and lower jaws are first separated using the integral intensity projection and then a region containing the desired edge lines are defined. In the final stage, some morphological operations are applied to isolate the teeth based on separating edge lines. The evaluation of the teeth segmentation is measured by isolating accuracy and visual inspection. Experimental results with 90.6% isolation accuracy of total 681 teeth illustrate that the proposed method is more efficient that the existing algorithms.
引用
收藏
页码:421 / 426
页数:6
相关论文
共 20 条
  • [1] Abdel-Mottaleb M, 2003, Proceedings of the 46th IEEE International Midwest Symposium on Circuits & Systems, Vols 1-3, P411
  • [2] Aeini Faraein, 2010, 3 INT C ADV COMP THE
  • [3] A New Approach to Teeth Segmentation
    Al-sherif, Nourdin
    Guo, Guodong
    Ammar, Hany H.
    [J]. 2012 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2012, : 145 - 148
  • [4] Fahmy G, 2004, LECT NOTES COMPUT SC, V3072, P789
  • [5] Gonzalez R. C., 2017, Digital Image Processing, V4th
  • [6] Kumar K, 2014, 2014 11TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP), P261, DOI 10.1109/ICCWAMTIP.2014.7073404
  • [7] Li CM, 2008, LECT NOTES COMPUT SC, V5242, P1083
  • [8] Mahoor MH, 2004, IEEE IMAGE PROC, P3475
  • [9] Classification and numbering of teeth in dental bitewing images
    Mahoor, MH
    Abdel-Mottaleb, M
    [J]. PATTERN RECOGNITION, 2005, 38 (04) : 577 - 586
  • [10] Mehta Darshan Bhavesh, 2016, INT J INNOVATIVE RES, P6796, DOI [10.15680/IJIRCCE.2016. 0404073, DOI 10.15680/IJIRCCE.2016.0404073]