1. Tanmoy Mondal, 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. 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 MATERIALS

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 method is 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 Nonmaximum suppression 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  nonedge pixel 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