However, as we
show in this paper, area-based approaches face certain
limitations, especially in analyzing superimposed spots.
The optical density (OD), i.e. the intensity of the most
intense pixel of a spot, can also be used to quantify
spots [8]. A different quantification approach is to find a
fitted function for each spot and calculate the volume
under the surface (VUS) of the function curve. Fitting of
2D-Gaussian function curves has been used for the
quantification of 2-DE spots for a long time [9,10], despite
the fact that the adequate functions to model 2-DE
spots continue to be contentious [1,11]. Recent developments
include a method to improve the applicability of
Gaussian functions for saturated spots [12] or the use of
2D-gaussian function curves in the creation of synthetic
gel images for the evaluation of image analysis algorithms
[13]. In this study, we propose a simple algorithm that fits
Gaussian functions to detected spots. We introduce the
novel concept of compound fitting, i.e. the simultaneous
fitting of neighboring groups of spots, for computationally
efficient resolution of overlapping spots. Furthermore,
we conduct the, to our knowledge, first structured comparison
between different spot quantification approaches,
namely the use of optical density of a spot, area-based
quantification and Gaussian function fitting. The proposed
compound fitting method is a highly accurate system for
spot quantification and we demonstrate its superior performance
relative to other methods for both synthetic and
real data