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 2015/11/281

Outline  Introduction  Segmentation approach  Result  Conclusion 2015/11/282

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 2015/11/283

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 2015/11/284

Introduction (3/6)  Polyps  Potentially cancerous  More than 5 mm  Consider potentially malignant  Need to be removed 2015/11/285

Introduction (4/6)  Physical colon cleansing  Large amounts of liquids  Medications  Enemas 2015/11/286

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 2015/11/287

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 2015/11/288

Outline  Introduction  Segmentation approach  Result  Conclusion 2015/11/289

Segmentation approach  Threshold  Morphological operations  Proposed approach 2015/11/2810

Threshold  Human abdomen  High density materials  Bone  Fluid  Stool  Soft tissue  Air 2015/11/2811

Threshold  Disadvantages  Not remove PVE voxels  Sensitive for each range of intensities  Gives rise to aliasing effects at the inner colon boundary 2015/11/2812 Fig.1Fig.2Fig.3

Segmentation approach  Threshold  Morphological operations  Proposed approach 2015/11/2813

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 2015/11/2814

Morphological operations  Dilation  The dilation of A by B  B is the structuring element 2015/11/2815

Morphological operations  Erosion  The Erosion of A by B  B is the structuring element 2015/11/2816

+ + 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 2015/11/2817

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 2015/11/2818

Segmentation approach  Threshold  Morphological operations  Proposed approach 2015/11/2819

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 2015/11/2820

Proposed approach  Approximate intensity based classification 2015/11/2821

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 2015/11/2822

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 2015/11/2823

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 2015/11/2824

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 2015/11/2825 +

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 2015/11/2826

Outline  Introduction  Segmentation approach  Result  Conclusion 2015/11/2827

Result  Virtual colonoscopy system  Automatic  Histogram classification  Seed point detection  A fully automatic solution  Segmentation  Digital colon cleansing 2015/11/2828

Result  Crux of this paper algorithm  Characterizing the intersections  Accurate a result as a manual segmentation  Not miss even a single intersection 2015/11/2829

Result  A cross-section of the CT data showing colon 2015/11/2830 (L)(R)

Result  Volume rendered images showing 2015/11/2831 (L)(R)

Outline  Introduction  Segmentation approach  Result  Conclusion 2015/11/2832

Conclusion  Advantages  Fast and accurate segmentation algorithm  Remove the partial volume effect  General algorithm  Use by any application similar to virtual colonoscopy 2015/11/2833

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 2015/11/2834

THE END Thank you for listening 2015/11/2835