Multiscale data reduction with flexible saliency criterion for biological image analysis.
Analysis of biomedical images requires attention to image features that represent a small fraction of the total image size. A rapid method for eliminating unnecessary detail, analogous to pre-attentive processing in biological vision, allows computational resources to be applied where most needed for higher-level analysis. In this report we describe a method for bottom up merging of pixels into larger units based on flexible saliency criteria using a method similar to structured adaptive grid methods used for solving differential equations on physical domains. While creating a multiscale quadtree representation of the image, a saliency test is applied to prune the tree to eliminate unneeded details, resulting in an image with adaptive resolution. This method may be used as a first step for image segmentation and analysis and is inherently parallel, enabling implementation on programmable hardware or distributed memory clusters.
Bosl
William J
WJ
Children's Hospital Boston Informatics Program at Harvard-MIT Division of Health Sciences and Technology, Boston, MA 02115, USA. william.bosl@childrens.harvard.edu
D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D003201:Computers; D044962:Data Compression; D003952:Diagnostic Imaging / Q000379:methods*; D005724:Ganglia / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008854:Microscopy, Electron; D015233:Models, Statistical; D014584:User-Computer Interface