Electrophoresis 2D gel residual encoding using adaptive wavelets for image segmentation

Adaptive wavelet image processing segmentation techniques are applied to 2D protein gel electrophoresis (2DE) gray scale images in order to simplify the laborious process of manually locating and quantifying protein spots (PS). A unique wavelet based residual encoding technique is shown to find differentially expressed low level PS when immersed in non-uniform locally varying background pixel intensity levels and/or when collocated with their high intensity counterparts. Additionally, a comparison of the results of the residual encoding versus two commercial proteomic toolsets is presented. In general, first an area of increased intensity must be recognized as a possible PS so that its intensity can be determined and its location identified. The second step is the matching of multiple PS representing the same protein on different gels so abundance comparisons can be made. PS segmentation is significant in that secondary abundance algorithms rely on the accuracy of the data produced by the segmentation stage. If the segmentation stage can be automated in a consistent, accurate, and efficient manner it will reduce false-positive PS recognition error rates and the need for manual intervention during the abundance process.