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Automatic Craniofacial Structure Detection on Cephalometric Images Tanmoy Mondal, Ashish Jain, and H. K. Sardana

Automatic Craniofacial Structure Detection on Cephalometric Images

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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 - PowerPoint PPT Presentation

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Page 1: Automatic Craniofacial Structure Detection on Cephalometric  Images

Automatic Craniofacial Structure Detection onCephalometric Images

Tanmoy Mondal, Ashish Jain, and H. K. Sardana

Page 2: Automatic Craniofacial Structure Detection on Cephalometric  Images

Introdutionthe 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

Page 3: Automatic Craniofacial Structure Detection on Cephalometric  Images

introduction

Page 4: Automatic Craniofacial Structure Detection on Cephalometric  Images

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

Page 5: Automatic Craniofacial Structure Detection on Cephalometric  Images

MethodsRegion DetectionAdaptive Nonlocal FilteringModification of Canny’s Edge

Detection AlgorithmEdge LinkingEdge Tracking Module

Page 6: Automatic Craniofacial Structure Detection on Cephalometric  Images

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

Page 7: Automatic Craniofacial Structure Detection on Cephalometric  Images

Region Detection

Page 8: Automatic Craniofacial Structure Detection on Cephalometric  Images

Modification of Canny’s Edge Detection Algorithm

Canny’s Edge Detectionspatial 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 )

Page 9: Automatic Craniofacial Structure Detection on Cephalometric  Images

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

Page 10: Automatic Craniofacial Structure Detection on Cephalometric  Images

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.

Page 11: Automatic Craniofacial Structure Detection on Cephalometric  Images

Modification of Canny’s Edge DetectionStep 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

Page 12: Automatic Craniofacial Structure Detection on Cephalometric  Images

Edge Linkingfor 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

Page 13: Automatic Craniofacial Structure Detection on Cephalometric  Images

Edge Linking

Step 1) form a difference imageStep 2) Determine the location of end points

in the high image. Mark that location as the edge point in the difference imageStep 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

Page 14: Automatic Craniofacial Structure Detection on Cephalometric  Images

Edge Linking

Page 15: Automatic Craniofacial Structure Detection on Cephalometric  Images

RESULTSresults 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

Page 16: Automatic Craniofacial Structure Detection on Cephalometric  Images

RESULT

Page 17: Automatic Craniofacial Structure Detection on Cephalometric  Images

THE END

thanks for your listening