Localised manifold learning for cardiac image analysis

Kanwal K. Bhatia; Anthony N. Price; Jo V. Hajnal; Daniel Rueckert

doi:10.1117/12.911455

Translator Disclaimer

14 February 2012 Localised manifold learning for cardiac image analysis

Kanwal K. Bhatia, Anthony N. Price, Jo V. Hajnal, Daniel Rueckert

Author Affiliations +

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83140H (2012) https://doi.org/10.1117/12.911455
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Manifold learning is increasingly being used to discover the underlying structure of medical image data. Traditional approaches operate on whole images with a single measure of similarity used to compare entire images. In this way, information on the locality of differences is lost and smaller trends may be masked by dominant global differences. In this paper, we propose the use of multiple local manifolds to analyse regions of images without any prior knowledge of which regions are important. Localised manifolds are created by partitioning images into regular subsections with a manifold constructed for each patch. We propose a framework for incorporating information from the neighbours of each patch to calculate a coherent embedding. This generates a simultaneous dimensionality reduction of all patches and results in the creation of embeddings which are spatially-varying. Additionally, a hierarchical method is presented to enable a multi-scale embedding solution. We use this to extract spatially-varying respiratory and cardiac motions from cardiac MRI. Although there is a complex interplay between these motions, we show how they can be separated on a regional basis. We demonstrate the utility of the localised joint embedding over a global embedding of whole images and over embedding individual patches independently.

Citation Download Citation

Kanwal K. Bhatia, Anthony N. Price, Jo V. Hajnal, and Daniel Rueckert "Localised manifold learning for cardiac image analysis", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140H (14 February 2012); https://doi.org/10.1117/12.911455

ACCESS THE FULL ARTICLE