Statistical image-domain multimaterial decomposition for dual-energy CT

Iterative Reconstruction for Quantitative Tissue Decomposition in Dual-Energy CT

Quantitative tissue classification using dual-energy CT has the potential to improve accuracy in radiation therapy dose planning as it provides more information about material composition of scanned objects than the currently used methods based on single-energy CT. One problem that hinders successful application of both singleand dualenergy CT is the presence of beam hardening and scatter artif...

Statistical analysis of Multi-Material Components using Dual Energy CT

This work describes a novel method for statistical analysis of multi-material components. The application scenario is industrial 3D X-ray computed tomography, emphasizing metrology of artefact affected plastics-metal components. The presented work makes use of dual energy CT data acquisition for artefact reduction, in order to optimize CT scans of multi-material components. Using statistical an...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models

Sample-oriented Domain Adaptation for Image Classification

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...

Dual-Energy CT for the Musculoskeletal System.

The principal advantages of dual-energy computed tomography (CT) over conventional CT in the musculoskeletal setting relate to the additional information provided regarding tissue composition, artifact reduction, and image optimization. This article discusses the manifestations of these in clinical practice-urate and bone marrow edema detection, metal artifact reduction, and tendon analysis, wi...