3d digital cleansing using segmentation rays
Download
Skip this Video
Download Presentation
3D Digital Cleansing Using Segmentation Rays

Loading in 2 Seconds...

play fullscreen
1 / 35

3D Digital Cleansing Using Segmentation Rays - PowerPoint PPT Presentation


  • 107 Views
  • Uploaded on

3D Digital Cleansing Using Segmentation Rays. Authors: Sarang Lakare, Ming Wan, Mie Sato and Arie Kaufman Source: In Proceedings of the IEEE Visualization Conference, pp.37–44, 2000 Speaker: Wen-Ping Chuang Adviser: Ku-Yaw Chang. Outline. Introduction

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' 3D Digital Cleansing Using Segmentation Rays' - reed


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
3d digital cleansing using segmentation rays

3D Digital CleansingUsing Segmentation Rays

Authors: Sarang Lakare, Ming Wan, Mie Sato

and Arie Kaufman

Source: In Proceedings of the IEEE Visualization

Conference, pp.37–44, 2000

Speaker: Wen-Ping Chuang

Adviser: Ku-Yaw Chang

outline
Outline
  • Introduction
  • Segmentation approach
  • Result
  • Conclusion
introduction 1 6
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
introduction 2 6
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

enhancement

introduction 3 6
Introduction(3/6)
  • Polyps
    • Potentially cancerous
    • More than 5 mm
      • Consider potentially malignant
      • Need to be removed
introduction 4 6
Introduction(4/6)
  • Physical colon cleansing
    • Large amounts of liquids
    • Medications
    • Enemas
introduction 5 6
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
introduction 6 6
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
outline1
Outline
  • Introduction
  • Segmentation approach
  • Result
  • Conclusion
segmentation approach
Segmentation approach
  • Threshold
  • Morphological operations
  • Proposed approach
threshold
Threshold
  • Human abdomen
    • High density materials
      • Bone
      • Fluid
      • Stool
    • Soft tissue
    • Air
threshold1
Threshold
  • Disadvantages
    • Not remove PVE voxels
    • Sensitive for each range of intensities
    • Gives rise to aliasing effects at the inner colon boundary

Fig.1

Fig.2

Fig.3

segmentation approach1
Segmentation approach
  • Threshold
  • Morphological operations
  • Proposed approach
morphological operations
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
morphological operations1
Morphological operations
  • Dilation
    • The dilation of A by B
      • B is the structuring element
morphological operations2
Morphological operations
  • Erosion
    • The Erosion of A by B
      • B is the structuring element
morphological operations3
Morphological operations
  • Highly twistedaffect the inner contour of the colon
  • Dilate followed by erode
    • Can fill in holes
  • Erode followed by dilate
    • Can remove noise

+

+

morphological operations4
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
segmentation approach2
Segmentation approach
  • Threshold
  • Morphological operations
  • Proposed approach
proposed approach
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
proposed approach1
Proposed approach
  • Approximate intensity based classification
proposed approach2
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
proposed approach3
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
proposed approach4
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
proposed approach5
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

+

proposed approach6
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
outline2
Outline
  • Introduction
  • Segmentation approach
  • Result
  • Conclusion
result
Result
  • Virtual colonoscopy system
    • Automatic
      • Histogram classification
      • Seed point detection
    • A fully automatic solution
      • Segmentation
      • Digital colon cleansing
result1
Result
  • Crux of this paper algorithm
    • Characterizing the intersections
  • Accurate a result as a manual segmentation
    • Not miss even a single intersection
result2
Result
  • A cross-section of the CT data showing colon

(L)

(R)

result3
Result
  • Volume rendered images showing

(L)

(R)

outline3
Outline
  • Introduction
  • Segmentation approach
  • Result
  • Conclusion
conclusion
Conclusion
  • Advantages
    • Fast and accurate segmentation algorithm
      • Remove the partial volume effect
    • General algorithm
      • Use by any application similar to virtual colonoscopy
conclusion1
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
ad