Volume Analysis Using Multimodal Surface Similarity Multimodal - PowerPoint PPT Presentation

Volume Analysis Using Multimodal Surface Similarity Multimodal Surface Similarity Martin Haidacher Stefan Bruckner and Martin Haidacher, Stefan Bruckner, and M. Eduard Gröller Institute of Computer Graphics and Algorithms Vienna University of Technology

Motivation Multimodal data: Same object different acquisition techniques Same object, different acquisition techniques One modality evens out drawback of the other Martin Haidacher 1

Motivation Multimodal data: Same object different acquisition techniques Same object, different acquisition techniques One modality evens out drawback of the other Martin Haidacher 2

Motivation Multimodal visualization: Side by side view Side-by-side view Difficult for comparison of both modalities Volumetric fusion Differences and/or similarities between Differences and/or similarities between modalities vanish through fusion Using similarity information to analyze and Using similarity information to analyze and visualize multimodal data Similarity of isosurfaces for combinations of isovalues Martin Haidacher 3

Multimodal Similarity Map (MSM) 4 Martin Haidacher

Multimodal Similarity Map (MSM) 5 Martin Haidacher

6 MSM Calculation Martin Haidacher

7 MSM Calculation Martin Haidacher

8 MSM Calculation Martin Haidacher

9 MSM Calculation Martin Haidacher

10 MSM Calculation Martin Haidacher

11 MSM Calculation Martin Haidacher

12 MSM Example Martin Haidacher

Using Multimodal Similarity Maps Applications for multimodal similarity map: Similarity based exploration Similarity-based exploration Multimodal similarity map as guidance map Maximum similarity isosurfaces Comparison of isosurfaces from two Comparison of isosurfaces from two modalities Similarity based classification Similarity-based classification Directly classify multimodal data based on the multimodal similarity map l i d l i il i Martin Haidacher 13

Similarity-Based Exploration The multimodal similarity map can be used to detect important structures detect important structures E.g. regions of high similarity Guidance map for the classification CT CT MRI MRI Martin Haidacher 14

Similarity-Based Exploration 15 Martin Haidacher

Similarity-Based Exploration 16 Martin Haidacher

Similarity-Based Exploration Similarity-Based Weighting Use similarity value to manipulate opacity Use similarity value to manipulate opacity Martin Haidacher 17

Similarity-Based Exploration Similarity-Based Weighting Use similarity value to manipulate opacity Use similarity value to manipulate opacity Martin Haidacher 18

Maximum Similarity Isosurfaces Using multimodal similarity map to find most similar isosurface similar isosurface One isovalue for one modality is given Lookup in the MSM provides isovalue for most similar isosurface in modality 2 y Useful for finding differences in both modalities modalities E.g. artifacts Martin Haidacher 19

Maximum Similarity Isosurfaces 20 Martin Haidacher

Similarity-Based Classification Classify multimodal data directly in the multimodal similarity map multimodal similarity map Individual transfer functions are not necessary User defines set of control points Combination of isovalues is classified with Combination of isovalues is classified with control point which is most similar Metric is based on similarity values Martin Haidacher 21

Similarity-Based Classification 22 h i h i . k k Martin Haidacher h i .. l

Similarity-Based Classification 23 Martin Haidacher

Similarity-Based Classification Generate clusters based on user-specified control points c control points c i Calculate the cluster centroids h i and use these points to finally generate the clusters The original control point c i is the centroid of The original control point c i is the centroid of this cluster M More intuitive user interaction i t iti i t ti Martin Haidacher 24

Similarity-Based Classification 25 Martin Haidacher

Similarity-Based Classification 26 Martin Haidacher

Conclusion Multimodal similarity map can be used to analyze multimodal data analyze multimodal data Detect similarities/differences in two modalities A sub-sampled version of the volumes can be used for calculation used for calculation Reduce calculation time to seconds MSM can either be used as guidance map in an existing framework or to classify multimodal g y data directly Martin Haidacher 27

Thank you! Thank you! Questions?