Automatic lane detection in chromatography images

This paper proposes a method for automating the detection of lanes in Thin-Layer Chromatography images. Our approach includes a preprocessing step to detect the image region of interest, followed by background estimation and removal. This image is then projected onto the horizontal direction to integrate the information into a one-dimensional profile. A smoothing filter is applied to this profile and the outcome is the input of the lane detection process, which is performed in three phases. The first one aims at obtaining an initial set of candidate lanes that are further validated or removed in the second phase. The last phase is a refinement step that allows the inclusion of lanes that are not clearly distinguishable in the profile and that were not included in the initial set. The method was evaluated in 66 chromatography images and achieved values of recall, precision and F ß -measure of 97.0%, 99.4% and 98.2%, respectively

Lane background removal in thin-layer chromatography images using continuous wavelet

This paper describes a new methodology to remove the background of the lanesin Thin-Layer Chromatography (TLC) images aiming at improving subsequentband detection. The storage of the biological samples to be analysed by TLC isusually done via plastic containers. Filter paper is an alternative that allowsreduced costs and higher portability, but with consequences in the image analysisstage due to a lane background alteration. In order to overcome this problem, anegative control lane is generated in every chromatographic plate. After preprocessingand lane detection stages a one-dimensional intensity profile is usedfor integrating lane information and the background influence is removed withthe help of the Continuous Wavelet Transform (CWT) decomposition. Theproposed method was tested in 78 lane images, A band detection algorithm wasapplied on lane profiles, and a superior detection rate was achieved for thebackground removed lanes

Correction of geometrical distortions in bands of chromatography images

This paper presents a methodology for correcting band distortions in Thin-LayerChromatography (TLC) images. After the segmentation of image lanes, theintensity profile of each lane column is spatially aligned with a reference profileusing a modified version of the Correlation Optimized Warping (COW)algorithm. The proposed band correction methodology was assessed using 105profiles of TLC lanes. A set of features for band characterization was extractedfrom each lane profile, before and after band distortion correction, and was usedas input for three distinct one-class classifiers aiming at band identification. In allcases, the best results of band classification were obtained for the set lanes afterband distortion correction