STMI 2014 - International Workshop on Sparsity Techniques in Medical Imaging

Sparsity based compressive sensing and sparse learning have been widely investigated and applied in machine learning, computer vision, computer graphics and medical imaging. In the medical community, these methods have been used successfully to speed up MR scan time, MR image reconstruction, organ segmentation from CT and MRI and classification methods for diseases. Sparsity Techniques in Medical Imaging (STMI2014) is the second in a series of workshops on this topic in conjunction with MICCAI 2014. This workshop focuses on major trends and challenges in this area, and aims to identify cutting-edge research work with important potential impact in medical imaging.
Objective
The goal of this workshop is to advance scientific research in sparse methods for medical imaging. It will foster dialogue and debate in this relatively new field which includes Compressive Sensing (CS), Sparse Learning (SL) and their applications to medical imaging. The technical program will consist of previously unpublished and invited papers, with substantial time allocated for discussion.
Topics
This workshop will include, but is not limited to the following topics on sparse methods:
Methodology:
Efficient Sparse Learning
Dictionary Learning
Shape Prior Modeling
Convex Optimization on Sparsity Priors
Group Sparsity
Structured Sparsity
Low-rank Matrix Structures
Large-scale Sparse Learning
Multi-Source Sparse Learning
Statistical Analysis
Model Selection, etc.
Applications:
Image / Signal Reconstruction
Image Segmentation
Image Enhancement
Image Registration
Compressed Sensing Magnetic Resonance Imaging
Anomaly Detection and Correction