Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Models and Image Processing
Asynchronous Iterative Methods for Multiprocessors
Journal of the ACM (JACM)
A Simple and Efficient Connected Components Labeling Algorithm
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Parallel Asynchronous Watershed Algorithm-Architecture
IEEE Transactions on Parallel and Distributed Systems
Size-based Transfer Functions: A New Volume Exploration Technique
IEEE Transactions on Visualization and Computer Graphics
Tuned and wildly asynchronous stencil kernels for hybrid CPU/GPU systems
Proceedings of the 23rd international conference on Supercomputing
Parallel graph component labelling with GPUs and CUDA
Parallel Computing
Expert Systems with Applications: An International Journal
A parallel cellular automata with label priors for interactive brain tumor segmentation
CBMS '10 Proceedings of the 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems
PACIFICVIS '12 Proceedings of the 2012 IEEE Pacific Visualization Symposium
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Brainbow is a genetic engineering technique that randomly colorizes cells. Biological samples processed with this technique and imaged with confocal microscopy have distinctive colors for individual cells. Complex cellular structures can then be easily visualized. However, the complexity of the Brainbow technique limits its applications. In practice, most confocal microscopy scans use different florescence staining with typically at most three distinct cellular structures. These structures are often packed and obscure each other in rendered images making analysis difficult. In this paper, we leverage a process known as GPU framebuffer feedback loops to synthesize Brainbow-like images. In addition, we incorporate ID shuffling and Monte-Carlo sampling into our technique, so that it can be applied to single-channel confocal microscopy data. The synthesized Brainbow images are presented to domain experts with positive feedback. A user survey demonstrates that our synthetic Brainbow technique improves visualizations of volume data with complex structures for biologists.