Salient Contour Extraction Using Contour Tee Bong-Soo Sohn Assistant Professor School of Computer Engineering Chung-Ang University
Motivation Isocontouring I(w) = {(x,y,z) | F(x,y,z) = w}, (F: input function, w: isovalue) one of the most popular modeling & visualization method <medical> <bio-molecular>
Motivation Infinitely many isocontours defined in an image An isocontour may have many contours Contour Connected component of an isocontour Often represents an independent structure Ex) mammogram (X-ray exam of female breast)
Motivation Salient Contour Extraction Useful for segmentation, analysis and visualization of regions of interest Can be applied to CAD(Computer Aided Diagnosis) for detecting suspicious regions breast boundary pectoral muscle mass (tumor) dense tissue dense tissue
3D Examples <Head MRI> <isocontour> <ventricle contour> <mass segmentation from breast MRI>
Past Contour Tree Approach Represents topological changes of contours according to isovalue change. Property structure (topology) of level sets contour extraction seed set generation for fast extraction
Our Approach Interactive Contour Tree Interface Performance Improvement of Extraction Process Utilizing Quantitative Information Development of Saliency Metric MND(Minimum Nesting Depth) Apply to medical images
Hybrid Parallel Contour Extraction Different from isocontour extraction Divide contour extraction process into Propagation Iterative algorithm -> hard to optimize using GPU multi-threaded algorithm executed in multi-core CPU Triangulation CUDA implementation executed in many-core GPU < propagation > < performance of our hybrid parallel algorithm >
Interactive Interface with Quantitative Information Geometric Property as saliency level Gradient(color) + Area (thickness)
Saliency Metric Minimum Nesting Depth (MND) Measured for each node of contour tree MND = min (depth from current node to terminal node of every subtree) High MND contour represents the boundaries of distinctive regions with abrupt intensity changes retaining the same topology Successfully applied to mass detection from 400 mammograms in USF database. [co-work with Prof. B.-W. Hong]