Outline Feature Extraction of Positron Image Based on a 3D Anisotropic Convolution Operator

Abstract

This study investigates the application of positron annihilation techniques to the interior of dense metal cavities for Three-dimensional (3D) imaging and extraction of contour features inside the cavities. A feature extraction algorithm based on a 3D anisotropic convolutional operator is proposed for profile feature extraction in low-resolution, low-contrast, and low-signal-to-noise positron image. First, aiming at the problem of positron image noise caused by inconsistent detectors and metal scattering effects, an image preprocessing algorithm combining filtering and full pixel correction is proposed. A 3D anisotropic convolution operator is then designed to extract contour features. To solve the contour feature discontinuity in the extracted contour feature, a 3D path search algorithm is proposed to obtain the centroid coordinate set of the contour feature, and then the centroid coordinate set is subjected to 3D curve fitting to obtain a smooth and continuous contour feature. The study is carried out on the raw 16-bit Digital Imaging and Communications in Medicine (DICOM) data of the positron image, and the data are processed from a 3D perspective, taking full advantage of the correlation between slices in the 3D positron image. In the actual testing, positron images with different kinds of foreign objects in the cavity are extracted using a 3D anisotropic convolution operator, and the contour feature extraction resolution reaches 2 mm.

Keywords

• Contour feature extraction
• 3D filter
• 3D anisotropic convolutional operator
• positron annihilation