Detection of Overlapping Teeth on Dental Panoramic Radiograph

Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.

[1]  Achmad Nizar Hidayanto,et al.  Segmentation of overlapping cytoplasm and overlapped areas in Pap smear images , 2017, 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA).

[2]  Nanik Suciati,et al.  Automatic Segmentation of Mandibular Cortical Bone on Cone-Beam CT Images Based on Histogram Thresholding and Polynomial Fitting , 2019 .

[3]  Myoung Nam Kim,et al.  Individual tooth region segmentation using modified watershed algorithm with morphological characteristic. , 2014, Bio-medical materials and engineering.

[4]  H.H. Ammar,et al.  Challenges of developing an automated dental identification system , 2003, 2003 46th Midwest Symposium on Circuits and Systems.

[5]  P. L. Lin,et al.  An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information , 2010, Pattern Recognit..

[6]  Ping Wang,et al.  A novel overlapping mice macrophages images segmentation method , 2009, 2009 International Conference on Image Analysis and Signal Processing.

[7]  Xin Jin,et al.  Mean Shift , 2017, Encyclopedia of Machine Learning and Data Mining.

[8]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[10]  Agus Zainal Arifin,et al.  Image segmentation by histogram thresholding using hierarchical cluster analysis , 2006, Pattern Recognit. Lett..

[11]  Yan Hao,et al.  Image Segmentation Algorithms Overview , 2017, ArXiv.

[12]  Dini Adni Navastara,et al.  Teeth segmentation on dental panoramic radiographs using decimation-free directional filter bank thresholding and multistage adaptive thresholding , 2015, 2015 International Conference on Information & Communication Technology and Systems (ICTS).

[13]  Zulkifly Mohd Zaki,et al.  Sobel and Canny Edges Segmentations for the Dental Age Assessment , 2014, 2014 International Conference on Computer Assisted System in Health.

[14]  Zulkifly Mohd Zaki,et al.  Region of adaptive threshold segmentation between mean, median and otsu threshold for dental age assessment , 2014, 2014 International Conference on Computer, Communications, and Control Technology (I4CT).

[15]  Ales Procházka,et al.  Separation of overlapping dental objects using normal vectors to image region boundaries , 2015, 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM).

[16]  Izzati Muhimmah,et al.  Segmentation of Overlapping Cervical Cells in Normal Pap Smear Images Using Distance-Metric and Morphological Operation , 2017 .

[17]  Richard M. Dansereau,et al.  Random walks for image segmentation containing translucent overlapped objects , 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[18]  Theekapun Charoenpong,et al.  Teeth segmentation from dental x-ray image by template matching , 2016, 2016 9th Biomedical Engineering International Conference (BMEiCON).

[19]  Nanik Suciati,et al.  Region Merging Strategy Using Statistical Analysis for Interactive Image Segmentation on Dental Panoramic Radiographs , 2017 .

[20]  Kittipol Wisaeng,et al.  Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images , 2019, IEEE Access.

[21]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[22]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[23]  Wang Zhibin,et al.  Image segmentation of overlapping leaves based on Chan–Vese model and Sobel operator , 2017 .

[24]  Philippe Salembier,et al.  Flat zones filtering, connected operators, and filters by reconstruction , 1995, IEEE Trans. Image Process..

[25]  P.W. Huang,et al.  Teeth segmentation of dental periapical radiographs based on local singularity analysis , 2014, Comput. Methods Programs Biomed..

[26]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..