Fast search and localization algorithm based on human visual perception modeling: An application for fast localization of structures in mammograms

A computer algorithm for fast identification and localization of structures of interest in images is presented. The algorithm is based on the analysis of a reduced set of image neighborhoods selected randomly by a constrained sampling of an associated image map of much smaller spatial resolution. The general approach is demonstrated by estimating the relative location of the breast tissue on a dataset of 860 digitized mammographic images. The computational times and breast tissue localization error rates are reported for different reduced spatial resolution image maps and three different features used for the corresponding neighborhood analysis. Our results show significant improvement on the error rates and computational times obtained with our approach compared to a pixel intensity thresholding approach. The algorithm implementation is very simple, requires less computation time than the sequential processing of each one of the image elements in a raster pattern and can be easily included into a hierarchical image analysis model.

Duke Scholars

Author Ehsan Samei Radiology