Automatic Lane Correction in DGGE Images by Using Hybrid Genetic Algorithms

DGGE (denaturing gradient gel electrophoresis) images are a particular type of images obtained by electrophoresis, that are used with different purposes. One of them is to study microbial biodiversity. Processing of this kind of images is a quite difficult problem, affected by various factors. Among these factors, the noise and distortion affect the quality of images, and subsequently, accuracy in interpreting the data. One of the problems this process presents is that lanes on the image are not perfectly aligned, and so the automatic processing of these images, e.g., for detection and quantification of bands, is not reliable. We present some methods for processing DGGE images that allow to improve their quality and thereof, improving biological conclusions. Results obtained with pure genetic algorithms, genetic algorithms hybridized with Tabu Search and genetic algorithms combined with Simulated Annealing are presented.

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