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Wavelet Transformation for Dental X-ray Image Segmentation & Feature Extraction

Implementing wavelet transform in dental X-ray images to segment teeth, extract textural features, and determine disease severity. The paper showcases a technique that uses wavelet transformation for enhanced tooth recognition.

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Wavelet Transformation for Dental X-ray Image Segmentation & Feature Extraction

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  1. Wavelet Transformation and Feature extraction for Dental X- ray Radiographs PROJECT GUIDE: Mrs.R.KARTHIKA DEVI Asst Professor ECE Prepared by G.ANAND KUMAR S.VIJAYARAMAN M.VIVEK KUMAR

  2. Objective • To implement the wavelet transform based image segmentation of dental x-ray images and extracting wavelet based textural features from the teeth and finding the disease severity of the teeth

  3. Abstract Teeth segmentation of dental X-ray radiographs is the important step in teeth recognition. A segmentation is to partition teeth from other areas in dental X-ray images. This paper proposes wavelet transform (WT) to segment dental X-ray images. The result of segmentation is a teeth feature marking each pixel, such as edge detection from image segmented. And the features like color and texture are extracted from the teeth image using Glcm technique. And the disease severity of the teeth can be found

  4. EXISTING WORK

  5. BASE PAPER Wavelet transformation for X-ray Radiographs technique 2010 Eighth international conference Nongluk covavisaruch

  6. ALGORITHM: PANORAMIC IMAGE SEGMENTATION OF DISEASED TEETH GLCM FEATURE EXTRACTION CLASSIFICATION

  7. Why Panoramic X-Ray • We are dealing with Panoramic X-ray images because that shows the entire tooth in the mouth • It is also for detecting the position of fully emerged as well as emerging teeth, identifying impacted teeth, and aiding in the diagnosis of tumors.

  8. Why we go for wavelet based segmentation • Because it gives clarity information • Here fast Diagnosis is done

  9. Original image

  10. Wavelet transform output

  11. Morphological image processing • Morphological processing operates on set of pixels using set theory • Morphological dilation is the basis operation in mathematical morphological • It uses Structuring element for expanding the shapes

  12. Morphological Output

  13. Texture Recognition • It is the method of identifying the Textures in the image • We used GLCM technique in the identification of textures • Texture based features gives the results of the inner objects in the image that is not visible the eyes

  14. GLCM • GLCM is Grey level Co-occurrence Matrix • The GLCM is a tabulation of how often different combinations of pixel brightness values (grey levels) occur in an image. • GLCM is used to find the features such as Energy, Entropy, Homogeneity and Correlation

  15. GLCM input images Original teeth Diseased teeth

  16. NORMAL TEETH VALUES

  17. ABNORMAL TEETH VALUES

  18. CONCLUSION AND FUTURE WORK In this paper we present the wavelet based segmentation technique and extracting the features of the dental X-ray images using glcm technique and disease severity was found. The wavelet transform shows better result than thresholding technique of segmentation

  19. FUTURE WORK The wavelet transform based feature extraction can be used for finding and grading the other dental diseases like cavity.

  20. Reference Wavelet transformation for X-ray Radiographs technique 2010 Eighth international conference Nonglukcovavisaruch Shankar BhausahebNikam, SuneetaAgarwal, “Wavelet Energy Signature and GLCM Features-based fingerprint anti-spoofing”Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, Aug,2008. JuhaLemmetti , JuhaLatvale , HakanOktem , Karen EgiaZarian and JarkkoNiittylahti “Implementing wavelet transform for X-Ray ImageEnhancement using General Purpose Processors” , EURASIP Journal on Applied Signal Processing, Volume 2003 ,January 2003. Juha Lemmetti1, Juha Latvala1, Hakan Öktem2, Karen Egiazarian2, and JarkkoNiittylahti “Implementing wavelet transforms for X-ray image Enhancement using general purpose processors”Asp volume 2003 H Tamura, S Mori, T Yamawaki, “Textural Features Corresponding toVisual Perception”, IEEE Transaction on Systems, man andCybernetics, Vol. 8, No. 6,1978W.-K. Chen, Linear Networks andSystems(Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.

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