3D Digital CleansingUsing Segmentation Rays
Authors: Sarang Lakare, Ming Wan, Mie Sato and Arie KaufmanSource: In Proceedings of the IEEE Visualization Conference, pp.37–44, 2000Speaker: Wen-Ping ChuangAdviser: Ku-Yaw Chang
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Outline
Introduction Segmentation approach Result Conclusion
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Introduction(1/6)
Virtual screening techniques Volume rendering techniques have grown rapidly Interactive frame rates generate accurate results
Organs have complex structures Segmentation plays a very important role
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Introduction(2/6)
Segmentation Simple threshold
Get complicated due to partial volume effect Cause unwanted and non-existing surfaces
Combine the threshold
and flood-fill techniques Flexible Segmentation rays Volumetric contrast
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Introduction(3/6)
Polyps Potentially cancerous More than 5 mm
Consider potentially malignant Need to be removed
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Introduction(4/6)
Physical colon cleansing Large amounts of liquids Medications Enemas
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Introduction(5/6)
A friendly virtual colonoscopy system Bypass the colon physical cleansing Need for segmenting the residual material Give a clean colon to the rendering algorithm
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Introduction(6/6)
A new bowel preparation scheme Enhance the stool and fluid densities Take and reconstruct into a 3D dataset Partial volume effect
Have not a clear boundary Worsen situation
Finite resolution Low contrast
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Outline
Introduction Segmentation approach Result Conclusion
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Segmentation approach
Threshold Morphological operations Proposed approach
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Threshold
Human abdomen High density materials
Bone Fluid Stool
Soft tissue Air
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Threshold
Disadvantages Not remove PVE voxels Sensitive for each range of intensities Gives rise to aliasing effects at the inner colon
boundary
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Fig.1 Fig.2 Fig.3
Segmentation approach
Threshold Morphological operations Proposed approach
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Morphological operations
Succession operation Such as dilation and erosion Flood-fill on all the fluid and stool regions A sequence of dilates and erodes to remove the
PVE voxels
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Morphological operations
Dilation The dilation of A by B
B is the structuring element
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Morphological operations
Erosion The Erosion of A by B
B is the structuring element
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Morphological operations
Highly twisted affect the inner contour of the colon Dilate followed by erode
Can fill in holes Erode followed by dilate
Can remove noise
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Morphological operations
Disadvantages Task considering the large number of such
regions Require a lot of human intervention Slow down the entire process of segmentation Result in some fluid/stool regions being ignored
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Segmentation approach
Threshold Morphological operations Proposed approach
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Proposed approach
Approximate intensity based classification Classify the intensity values in the histogram
Depend on the number and type of district regions Region boundaries
Define by approximate thresholds Flexible
Unique intensity profiles at different intersections Study and store
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Proposed approach
Approximate intensity based classification
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Proposed approach
Region growing Detect and mark the interior AIR region
A smooth horizontal surface due to gravity Take a seed point to mark all the air voxels Reach no longer belong the air voxels
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Proposed approach
Selecting starting points for segmentation rays Critical to the overall speed of the algorithm Select fewer the voxels get faster the algorithm Assign the boundary voxels are simplest and
fastest
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Proposed approach
Detecting intersections using segmentation rays Critical to the detection of the polyps Remove most of the PVE voxels Give an improved colon contour
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Proposed approach
Segmentation rays From each of the AIR boundary voxel 26-connected-neighbor directions Stop and ignore
Not find any intersection after traversing a certain distance
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Proposed approach
Volumetric contrast enhancement A programmed transfer function
Unwanted materials are removed Similar to contrast enhancement
A smooth transfer function Get no-aliasing boundaries Improve the quality of volume rendering
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Outline
Introduction Segmentation approach Result Conclusion
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Result
Virtual colonoscopy system Automatic
Histogram classification Seed point detection
A fully automatic solution Segmentation Digital colon cleansing
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Result
Crux of this paper algorithm Characterizing the intersections
Accurate a result as a manual segmentation Not miss even a single intersection
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Result
A cross-section of the CT data showing colon
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(L) (R)
Result
Volume rendered images showing
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(L) (R)
Outline
Introduction Segmentation approach Result Conclusion
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Conclusion
Advantages Fast and accurate segmentation algorithm
Remove the partial volume effect General algorithm
Use by any application similar to virtual colonoscopy
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Conclusion
Future work Build an interactive segmentation system
Pick intersection characteristics using a mouse Find a particular intersection assigning
classification/reconstruction tasks to the rays Add visual feedback
Render and display the segmented dataset
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THE ENDThank you for listening
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