Background: Proteomics was built around two-dimensional (2D) gel electrophoresis.
Accurately analyzing the images generated from 2D gel electrophoresis for spot detection is a timeconsuming
process especially for high-resolution and large images.
Objective: In this paper, we present an accurate GPU-accelerated software tool for the detection and
quantification of protein spots in 2D gel electrophoresis images.
Method: We adopt pixel-based approach that employs wavelet relational fuzzy C-means clustering and
distance transform to detect and quantify the protein spots. This pixel-based spot detection approach is
more accurate than the contour-based approaches; however it is compute-intensive. So, along with
algorithmic optimizations, we present the mapping and optimization of the pixel-based spot detection
algorithm onto graphics processing units (GPUs); including NVIDIA and AMD GPUs.
Results: This approach is proved to exhibit better spot detection in quantitative comparisons with the
commercial software tools. Specifically, it achieves a degree of improvement in F-measure of 21.237%
and 11.716% on the average compared to Delta2D and Melanie, respectively. We carry out experiments
on images of large size and high resolution for healthy and diseased samples. Our implementation has
accomplished up to five orders of magnitude speedup compared to the single-threaded MATLAB
Conclusion: We proposed an accurate and efficient tool for detecting and quantifying the protein spots
in 2D Gel Electrophoresis images. Our tool outperforms commercial software tools in accuracy of
detecting protein spots while achieving significantly better performance than single-threaded MATLAB
implementation by utilizing the GPU accelerators.