Sparsity Techniques in Medical Imaging Special Issue of Computerized Medical Imaging and Graphics

With the advent of the age for big data and complex structure, sparsity has been an important modeling tool in compressed sensing, machine learning, image processing, neuroscience and statistics. In the medical imaging field, sparsity methods have been successfully used in image reconstruction, image enhancement, image segmentation, anomaly detection, disease classification, and image database retrieval. Developing more powerful sparsity models for a large range of medical imaging and medical image analysis problems as well as efficient optimization and learning algorithm will keep being a main research topic in this field.

Source: Computerized Medical Imaging and Graphics - July 15, 2015 Category: Radiology Authors: Tsuhan Chen, Ruogu Fang, Dimitris Metaxas, Pina Sanelli, Shaoting Zhang Tags: Editorial Preface Source Type: research