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

enhancement112/04/18 4

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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