Automatic lane detection and separation in one dimensional gel images using continuous wavelet transform

Abstract

This work presents new methods which automatically detect and separate the lanes in one dimensional electrophoresis gel images. The motivation is to present algorithms which can be applied on one dimensional gel images produced from separation of DNA, RNA, (including PCR products), proteins, etc. In addition, a one dimensional intensity profile for each separated lane analogous to capillary electrophoresis (CE) profiles is provided. Experiments are performed on ten multi- and ten single-locus DNA gel images, where the number of lanes, resolution and image quality are highly variable. The Elder & Southern (ES) semi-automatic lane detection method, followed by two new methods: an iterative moving average filter (IMA) and a continuous wavelet transform (CWT) with Morlet wavelet are presented and applied on the test images. The accuracy of the results obtained by applying these methods on a set of 10 multi-locus gel images was 91.7% with the ES, 94.4% with the IMA and 99.5% with the CWT; in the set of 10 single-locus images it was 100% with the ES, 94.8% with the IMA and 97.8% with the CWT; for both sets combined (20 images) it was 96.1% with the ES, 94.7% with the IMA and 98.7% with the CWT. Two lane separation methods are presented; the first is based on a K-neighbouring points algorithm, while a half-width algorithm is applied in the second method. Every separated lane from a gel image can be transferred into a sub-image and further into a one dimensional intensity profile.