Multi-resolution super-pixels and their applications on fluorescent mesenchymal stem cells images using 1-D SIFT merging

A new multi-resolution super-pixel based algorithm is proposed to track cell size, count and motion in Mesenchymal Stem Cells (MSCs) images. Multi-resolution super-pixels are obtained by placing varying density seeds on the image. The density of the seeds are determined according to the local high frequency components of the MSCs image. In this way a multi-resolution super-pixels decomposition of the image is obtained. A second contribution of the paper is novel decision rule for merging similar neighboring super-pixels. One-dimensional version of the well known scale invariant feature transform (SIFT) is developed and applied to the histograms of the neighboring super-pixels to determine similar regions. The proposed algorithm is experimentally shown to be successful in segmenting and tracking cells in MSCs images.