X-Ray CT Image Segmentation: Automatic Sandwich Structure Layer Separation Using Reduced Dimension Hough Transformation

摘要

Many structures in aerospace, semiconductor and precision engineering are multi-layer in nature. Examples include Low Temperature Co-Fire Ceramic (LTCC), PCBA, stacked IC, Through-Silicon-Via and composite materials for aircraft wings. Segmentation of each internal layer in any orientation is essential for layer alignment as well as delamination, disbond and warpage analysis. In this paper we propose a RDHT (Reduced Dimension Hough Transformation) for automatic layer detection. Instead of segmenting internal surfaces at voxel level, correlation based edge operator is applied to extract features in 3D space whereby the likelihood of any planar structure is associated with the number of features on a specific plane. We use Randomized Hough Transform to map 3D features in three one dimensional accumulators plus one verification accumulator to reduce Hough space dimension. The RDHT has been successfully applied to various objects to reveal internal planar structures. For a CT result with a 512×512×512 cube, the feature detection takes 30 seconds and the subsequent layer separation takes 12 seconds (laptop with Intel dual core 1.6G). We demonstrate that the algorithm can segment all 16 layers of a stacked IC with an accuracy of 0.5 voxel.