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Automatic Craniofacial Structure Detection on Cephalometric Images

Automatic Craniofacial Structure Detection on Cephalometric Images. Tanmoy Mondal , Ashish Jain, and H. K. Sardana. Introdution. the research advancement in the field of automatic detection of craniofacial structures has been portrayed ASM - did not give sufficient accuracy for

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Automatic Craniofacial Structure Detection on Cephalometric Images

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  1. Automatic Craniofacial Structure Detection onCephalometric Images TanmoyMondal, Ashish Jain, and H. K. Sardana

  2. Introdution • the research advancement in the field of automatic detection of craniofacial structures has been portrayed • ASM -did not give sufficient accuracy for landmark detection • AAM-results showed 25% accuracy improvement over ASM

  3. introduction

  4. MATERIALS • The cephalometric images were randomly selected without any judgement • Dataset 1 : 85 pretreatment cephalograms 2400 * 3000 pixels in DICOM • Dataset 2: 55 pretreatment cephalograms 1537 * 1171pixels in JPEG

  5. Methods • Region Detection • Adaptive Nonlocal Filtering • Modification of Canny’s Edge Detection Algorithm • Edge Linking • Edge Tracking Module

  6. Region Detection & Adaptive Nonlocal Filtering • applied an effective template matching approach • 2-D normalized cross correlation - major limitation of above methodis high computational cost • first this fixed tripod rod, which is common in every image, is detected • adaptive nonlocal filtering is performed on each region of interest

  7. Region Detection

  8. Modification of Canny’s Edge Detection Algorithm Canny’s Edge Detection • spatial gradient calculation is performed by the Gaussian kernel • Edge direction of pixel • Nonmaximumsuppression • a suitable pair of threshold values is selected to track the remaining pixels ( HTV and LTV )

  9. Canny’s Edge Detection • gradient > HTV  edge pixel • gradient > LTV nonedgepixel • LTV < gradient < HTV  edge pixel • Due to the local intensity variability and low contrast of the small desired curves against the background  failed to detect

  10. Modification of Canny’s Edge Detection • Step 1) location of the candidate points, and the magnitude of the entire pixel are selected. • Step 2) The Eigen value map of the image is generated • Step 3) A threshold value of the Eigen value map is selected as the ( maximum + minimum)/2 of the Eigen value matrix.

  11. Modification of Canny’s Edge Detection • Step 4) pixel with its corresponding Eigen value less than the threshold value, selected as local dynamic HTV • Step 5) Select new edge points in this locality using the local dynamic HTV and the global LTV

  12. Edge Linking • for joining the broken edge points. • use two edge images that have undergone hysteresis: a high image and a low image. • The main idea is to use the high image as guidance for promoting edges from the low image

  13. Edge Linking • Step 1) form a difference image • Step 2) Determine the location of end points in the high image. Mark that location as the edge point in the difference image • Step 3) search the neighborhood for any of them as edge pixel and whether it connects to another end point in the high image • Step 4) If a connection is discovered, then this traced edge in the difference image is qualified

  14. Edge Linking

  15. RESULTS • results obtained by the algorithm were compared with those obtained by the human experts. • if the particular structure is detected more than 80% of the required detection length of the structure  acceptable detection

  16. RESULT

  17. THE END thanks for your listening

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