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Surface Normal Overlap: A Computer-Aided Detection Algorithm With Application to Colonic Polyps and Lung Nodules in Helical CT
Authors: David S. Paik*, Christopher F. Beaulieu, Geoffrey D. Rubin, Burak Acar, R. Brooke Jeffrey, Jr., Judy Yee,Joyoni Dey, and Sandy Napel
Source: IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 23, NO. 6, JUNE 2004
Speaker: Wen-Ping ChuangAdviser: Ku-Yaw Chang
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Outline
Introduction CAD algorithm Theoretical analysis Conclusion
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Introduction
Lung cancer Lung Nodules
Colon cancer Colonic Polyps
Attention and eye fatigue Accuracy and efficiency
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Introduction
CAD methods Computed tomography images CT lung nodule detection CT colonic polyp detection
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Introduction
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Detecting lung nodulesSensiti
vityFPs
2D multilevel thresholding detection algorithm
94% 1.25
Multilevel thresholding and a rolling ball algorithm
70% 1.5
Patient-specific models 86% 11
An improved template-matching technique
72% 31
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Introduction
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Detecting colonic polypsSensiti
vityFPs
Measures abnormal wall thicknesses 73%9-90
Convolution-based partial derivatives
64% 3.5
Both prone and supine datasets 100% 2.0
Combined surface normal and sphere fitting methods
100% 8.2
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Introduction
Surface normal overlap method On 8 CT datasets
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Detection SizeSensiti
vityFPs
Colonic polyps
10mm and larger
100% 7.0
Lung nodules
6mm and larger 90% 5.6
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Outline
Introduction CAD algorithm Theoretical analysis Conclusion
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CAD algorithm
Pre-Processing and Segmentation
Gradient Orientation Surface Normal Overlap Candidate Lesion Selection
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Pre-Processing and Segmentation
CT volume data I(x,y,z)： (0.6mm)3
Reduce any bias Lesions at different orientations Datasets with different voxel sizes
Segmentation automatically Colon lumen Lung parenchyma
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Pre-Processing and Segmentation
Segmentation automatically (S1) All air intensity voxels
I(x,y,z) ＜ -700HU Negatively
any data volume connected to the edges width or depth of greater than 60 mm small air pockets
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Pre-Processing and Segmentation
Segmentation automatically (S2) Limit the remaining computations
reduces computational requirements eliminates FPs arising within soft tissue
structures Produce a 5mm thickened region
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CAD algorithm
Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection
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Gradient Orientation
Computes the image gradient vector High-contrast edges Determine the image surface normals
Reduced search space Resulting surface normal vectors
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CAD algorithm
Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection
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Surface Normal Overlap
Critical for detecting lesions Convex regions and surfaces
Surface normal vectors A dominant curvature along a single
direction polyps and nodules
Set 10mm of the projected surface normal vectors
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Surface Normal Overlap
Robustness Perfectly spherical objects Radial direction
allowing roughly globular objects to have a significant response
Transverse direction allowing nearly intersect surface normal
vectors to be additive
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CAD algorithm
Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection
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Candidate Lesion Selection
Complex anatomic structures Multiple convex surface patches Multiple local maxima
Smallest scale of the features Generate distinct local maxima Set to 10 mm
Sorted in decreasing order and recorded
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CAD algorithm
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Outline
Introduction CAD algorithm Theoretical analysis
Stochastic Anatomic Shape Model Model Parameter Estimation
Conclusion
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Stochastic Anatomic Shape Model
A simple parametric shape Add stochastically-governed variation Produce realistic anatomic shape
Nominal position Radius is random variables
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Stochastic Anatomic Shape Model
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真實的形狀
虛擬的圓形
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Model Parameter Estimation
Performing edge detection Identifying the surface normal
vectors nodule, polyp, vessel, fold
Finding the nominal sphere or cylinder
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Model Parameter Estimation
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Outline
Introduction CAD algorithm Theoretical analysis Conclusion
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Conclusion
A novel CAD algorithm Surface normal overlap method
Theoretical traits Statistical shape model
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Conclusion
Optimized the performance CT simulations A per-lesion cross-validation method
Provided a preliminary evaluation
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Conclusion
Ultimately envision The first step in a larger overall
detection scheme Intensive classifier Decrease the false positives rate
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THANK YOU FOR LISTENING.
The End
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