Cell Counting for In Vivo Flow Cytometer Signals Using Wavelet-Based Dynamic Peak Picking

We propose the development of new methods to analyze data produced by a so-called in vivo flow cytometer (IVFC). This technology allows to quantify numbers of specific cells in a living organism and is extraordinarily useful for the quantitative study of diseases such as cancer or other phenomena, including immunological processes. Existing computational methods for the analysis of IVFC signals are based on elementary signal processing and require manual user interaction. To overcome such limitations, we propose the development of improved algorithms that may quantify cells in a reliable and efficient manner, while eliminating the need for user interaction. To this end, we propose a method based on wavelet-based deonoising combined with a dynamic peak-picking procedure. This procedure proves to be reliable on real data, and eliminates the need for certain control experiments which were required for earlier approaches.