A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation

Dental X-ray image segmentation has an important role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoretically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solutions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and compared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.

[1]  A. Ross,et al.  Automatic Tooth Segmentation Using Active Contour Without Edges , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.

[2]  Zhicheng Ji,et al.  Dental plaque segmentation and quantification using histogram-aided fuzzy c-means algorithm , 2010, Proceedings of the 29th Chinese Control Conference.

[3]  Lequan Min,et al.  Novel modified fuzzy c-means algorithm with applications , 2009, Digit. Signal Process..

[4]  Lizhuang Ma,et al.  Interactive Tooth Segmentation of Dental Models , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[5]  C. Stolojescu-Crisan,et al.  A Comparison of X-Ray Image Segmentation Techniques , 2013 .

[6]  Lequan Min,et al.  Dental Plaque Quantification Using Cellular Neural Network-Based Image Segmentation , 2006 .

[7]  Mohamed Abdel-Mottaleb,et al.  Human Identification From Dental X-Ray Images Based on the Shape and Appearance of the Teeth , 2007, IEEE Transactions on Information Forensics and Security.

[8]  Witold Pedrycz,et al.  Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection , 2015, Comput. Geosci..

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

[10]  Asif Ekbal,et al.  A new semi-supervised clustering technique using multi-objective optimization , 2015, Applied Intelligence.

[11]  Sameer Singh,et al.  Advanced Algorithmic Approaches to Medical Image Segmentation , 2002, Advances in Computer Vision and Pattern Recognition.

[12]  Himansu Sekhar Behera,et al.  Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014 , 2015 .

[13]  Giovanna Castellano,et al.  Fuzzy mathematical morphology for biological image segmentation , 2014, Applied Intelligence.

[14]  Y. H. Lai,et al.  Effective Segmentation for Dental X-Ray Images Using Texture-Based Fuzzy Inference System , 2008, ACIVS.

[15]  Masatoshi Sakawa,et al.  An Interactive Fuzzy Satisficing Method for Multiobjective Stochastic Integer Programming with Simple Recourse , 2012 .

[16]  Mohammad H. Mahoor,et al.  Classification and numbering of teeth in dental bitewing images , 2005, Pattern Recognit..

[17]  Omaima Nomir,et al.  A system for human identification from X-ray dental radiographs , 2005, Pattern Recognit..

[18]  Le-Quan Min,et al.  Dental plaque quantification using FCM-based classification in HSI color space , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[19]  Ricardo J. G. B. Campello,et al.  Relative clustering validity criteria: A comparative overview , 2010 .

[20]  Hany H. Ammar,et al.  Teeth segmentation in digitized dental X-ray films using mathematical morphology , 2006, IEEE Transactions on Information Forensics and Security.

[21]  Lakhmi C. Jain,et al.  Introduction to Fuzzy Clustering , 2006 .

[22]  Lianghai Jin,et al.  Characteristic analysis of Otsu threshold and its applications , 2011, Pattern Recognit. Lett..

[23]  Amjad Rehman,et al.  Evaluation of Current Dental Radiographs Segmentation Approaches in Computer-aided Applications , 2013 .

[24]  M. Caramia,et al.  Multi-objective Management in Freight Logistics: Increasing Capacity, Service Level and Safety with Optimization Algorithms , 2008 .

[25]  Abdolvahab Ehsani Rad,et al.  Level Set and Morphological Operation Techniques in Application of Dental Image Segmentation , 2014 .

[26]  Witold Pedrycz,et al.  Data Clustering with Partial Supervision , 2005, Data Mining and Knowledge Discovery.

[27]  Endo Yasunori,et al.  On semi-supervised fuzzy c-means clustering , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[28]  Sim Heng Ong,et al.  Tooth segmentation of dental study models using range images , 2004, IEEE Transactions on Medical Imaging.

[29]  Ravi Janardan,et al.  Improved Segmentation of Teeth in Dental Models , 2011 .

[30]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  K. K. Rahini,et al.  Review of Image Segmentation Techniques: A Survey , 2014 .

[32]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[33]  Mohamed Abdel-Mottaleb,et al.  A content-based system for human identification based on bitewing dental X-ray images , 2005, Pattern Recognit..

[34]  Adam Krzyzak,et al.  An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments , 2006, Comput. Medical Imaging Graph..

[35]  Qi Huang,et al.  Semi-supervised fuzzy clustering with metric learning and entropy regularization , 2012, Knowl. Based Syst..

[36]  Masatoshi Sakawa,et al.  An Interactive Fuzzy Satisficing Method for Multiobjective Stochastic Integer Programming Problems through a Probability Maximization Model , 2005 .

[37]  Moncef Gabbouj,et al.  Feature selection for content-based image retrieval , 2008, Signal Image Video Process..

[38]  Tuan D. Pham,et al.  Segmentation of medical images using geo-theoretic distance matrix in fuzzy clustering , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[39]  C. Mohan,et al.  An interactive satisficing method for solving multiobjective mixed fuzzy-stochastic programming problems , 2001, Fuzzy Sets Syst..