2014 Summer Project Week:mipiX
Key Investigators
- Adrian Dalca, Ramesh Sridharan, Erjona Topalli, Polina Golland, MIT
Project Description
This is a new approach for fast and effective visualization of large image collections in population studies. The key insight is to collapse inherently high-dimensional imaging data onto an interactive two-dimensional canvas native to a computer screen in a way that enables intuitive browsing of the image data. Increasingly, medical image computing research involves exploring large image sets with high intrinsic dimensionality. This includes three dimensions for each medical volume, and many meta-dimensions such as subject index, modality type in multimodal studies, time in longitudinal studies, or parameter choice in parameter sweep experiments. Current visualization tools generally display one or few 2D slices or 3D renderings at a time, and do not provide a natural way to explore the meta-dimensions.
Objective
- We intend to incorporate several new features into the tool, including the display of appropriate information about the volume and support for individual masks for each volume.
Approach, Plan
- The development is done mostly in Javascript, maintained open-source at https://github.com/adalca/tipiX
