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.
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 The technical program will consist of previously unpublished and invited papers, with substantial time allocated for discussion.
This workshop will include, but is not limited to the following topics on sparse methods:
Papers are limited to eight pages. Papers should be formatted in Lecture Notes in Computer Science style (LNCS). STMI reviewing is double blind.
STMI is using an online submission system. The file format for submissions is PDF.
Supplemental material submission is optional.
June 15th, 2012: Full paper submission (23:59 Pacific Standard Time)
July 5th, 2012: Acceptance of papers
July 25th, 2012: Camera-ready Version
October 5th, 2012: One day workshop
The Conference Program
Three best papers in STMI2012.
Learning best wavelet packet bases for compressed sensing of classes of images: application to brain MR imaging, Michal Romaniuk*, Imperial College London, Anil Rao, Imperial College London, Robin Wolz, Imperial College London, Joseph Hajnal, King's College London, Daniel Rueckert, Imperial College London.
Robust Patch-Based Multi-Atlas Labeling by Joint Sparsity Regularization, Guorong Wu*, BRIC, UNC-CH, Qian Wang, BRIC, UNC-CH, Daoqiang Zhang, BRIC, UNC-CH, Dinggang Shen, UNC.
The Benefit of Tree Sparsity in Accelerated MRI, Chen Chen*, Uni. of Texas at Arlington, Junzhou Huang, UT Arlington.