automatic craniofacial structure detection on cephalometric images
DESCRIPTION
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 PresentationTRANSCRIPT
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Automatic Craniofacial Structure Detection onCephalometric Images
Tanmoy Mondal, Ashish Jain, and H. K. Sardana
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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
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introduction
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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
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MethodsRegion DetectionAdaptive Nonlocal FilteringModification of Canny’s Edge
Detection AlgorithmEdge LinkingEdge Tracking Module
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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
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Region Detection
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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 )
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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
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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.
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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
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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
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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
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Edge Linking
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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
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RESULT
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THE END
thanks for your listening