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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

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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



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