Total views : 252

Designing a Feature Vector for Statistical Texture Analysis of Mandibular Bone

Affiliations

  • Bharath University, Chennai - 600073, Tamil Nadu, India
  • Bharath University, Chennai - 600073, Tamil Nadu
  • Cummins College of Engineering for Women, Pune - 411052, Maharashtra, India

Abstract


Objectives: This paper develops an algorithm to generate automatically, a feature vector using various statistical texture features of trabecular mandible from digital Orthopantomogram (OPG). These statistical features are computed using Gray Level Co-occurrence Matrices (GLCM) of mandible structure. Analysis: For this paper, region of interest is trabecular mandible bone present in lower jaw. Mandible Segmented from OPG is taken as input to the algorithm. GLCM of this mandible is calculated at four angles viz., 00, 450, 900 and 1350 and for two gray scale intensities viz., 128 and 256. Various texture features are calculated from each of these GLCMs. These statistical features are compared and analyzed. Findings: 100 digital OPGs are used for analysis of statistical texture features. Four texture features contrast, viz., correlation, energy, homogeneity are calculated for the four angles mentioned above. Improvement: Texture analysis is performed on various types of images since last 50 years. Locating systemic disease features (e.g., Osteoporosis) from OPG is an upcoming research area. In this area, it is observed that comparatively less literature is available which uses GLCM texture features. The algorithm proposed here devices a texture feature vector for mandible. Analysis of the features is also presented in this paper

Keywords

Digital OPG, GLCM, Mandible, Texture Analysis

Full Text:

 |  (PDF views: 220)

References


  • Naik A, Tikhe S, Bhide DS, Kaliyamurthie KP, Saravanan T. Algorithm to detect fracture from OPG images using texture analysis. 6th IEEE International Advance Computing Conference, IACC; 2016. p. 374–85.
  • OPG Digital. Available from: http://www.phmedicalcentre.com/opg.asp
  • Mikulka J, Kabrda M, Gescheidtova E, Peina VC. Classification of jawbone cysts via orthopantomogram processing. 35th International Conference on Telecommunications and Signal Processing (TSP); 2012 Jul. p. 499–502.
  • Hayashi T, Matsumoto T, Sawagashira S, Tagami M, Katsumata AK, Hayashi Y, Muramatsu C, Zhou X. A new screening pathway for identifying asymptomatic patients using dental panoramic radiographs. Medical Imaging Computer-Aided Diagnosis. In van Ginneken B, Novak CL, editors. Proceedings of the SPIE. 2012; 8315:111–21.
  • Allen PD, Graham J, Farnell DJJ, Marjanovic EJ, Adams J, Jacobs R, Karayianni K, Lindh C, Stelt PFVD, Horner K, Devlin H. Detecting osteoporosis from dental radiographs using active shape models. IEEE International Symposium on Biomedical Imaging; 2007. p. 1256–9.
  • Amal RS, Mohammed M, Fatin Kh, Abbas A, Nuhad Al, Hassan H. Diagnostic efficacy of mandibular cortical thickness on panoramic radiographs to identify postmenopausal women with low bone mineral densities (Iraqi Population). Journal of American Science. 2013; 308–12.
  • Lee JS, Kim OS, Chung HJ, Kim YJ, Kweon SS, Lee YH, Shin MH, Yoon SJ. The prevalence and correlation of carotid artery calcification on panoramic radiographs and peripheral arterial disease in a population from the Republic of Korea: The Dong-gu study. Dentomaxillofacial Radiology the British Institute of Radiology. 2013; 42(1). 2972-5099.
  • Robert M, Haralick K, Shanmugam S, Dentein D. Texture features for Image classification. IEEE Transaction on Systems, Man and cybernetics. 1973; 3(6):610–21.
  • Sebastian B, Unnikrishnan A, Balakrishnan K. Grey level co-occurrence matrices: Generalisation and some new features. IJCSEIT. 2012; 2(2):151–7.
  • Kumaar MA, Thanaraj P. Feature extraction of arterio-venous malformation images using grey level co-occurrence matrix. Indian Journal of Science and Technology. 2015 Dec; 8(35):1–5.
  • Reshma CP, Chand J. Dheeba D. Automatic detection of color fundus retinopathy. Indian Journal of Science and Technology. 2015; 8(26):1–12.
  • Robert M, Haralick H. Statistical and structural approaches to texture. Proceedings of IEEE. 1979; 67(5):786–808.
  • Albregtsen F. Statistical texture measures computed from gray level coocurrence matrices. Image Processing Laboratory Department of Informatics University of Oslo; 2018. p. 1–14.
  • Naik A, Tikhe S, Bhide S, Kaliyamurthie KP, Saravanan T. Indian Journal of Science and Technology. 2016; 9(21):1–6.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.