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This paper presents a methodology for correcting band distortions in Thin-Layer Chromatography (TLC) images. After the segmentation of image lanes, the intensity profile of each lane column is spatially aligned with a reference profile using a modified version of the Correlation Optimized Warping (COW) algorithm. As the initial partition of the profile into equal length segments proposed by COW can result in the separation of a single band between two segments to be disjointedly aligned, in the proposed method the warping function is only applied to selected profile regions containing groups of adjacent bands. The proposed band correction methodology was assessed using 105 profiles of 105 TLC lanes. A set of features for band characterization was extracted from each lane profile, before and after band distortion correction, and was used as input for three distinct one-class classifiers aiming at band identification. In all cases, the best results of band classification were obtained for the set lanes after band distortion correction.

This paper describes a methodology to remove the background of the lanes in Thin Layer Chromatography (TLC) images, aiming at improving band detection and classification. The storage of the biological samples to be analyzed by TLC is usually done via plastic containers. Filter paper is an alternative that allows reduced costs and higher portability, but it increases the complexity of the image analysis stage due to lane background alteration. In order to overcome this problem, a negative control lane is included in every chromatographic plate. After image preprocessing and lane detection stages, a background profile is generated by processing the negative control lane using the Discrete Wavelet Transform (DWT). This profile is then subtracted to the profiles of all other sample lanes in order to overcome the data degradation introduced by filter paper usage. For assessing the proposed background removal process, 105 TLC lanes, with and without background, were used as input for three one-class classifiers. In all cases, the best results were achieved for the lanes after background removal.

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 Fβ-measure of 97.0%, 99.4% and 98.2%, respectively.

This paper proposes an automatic method for estimating the location of the optic disc in color images of the retina. The proposed methodology is founded in a new concept, the entropy of vascular directions, which proved to be a reliable measure for assessing the convergence of vessels around an image point. To improve the robustness of the method, the search for the maximum value of entropy is restricted to image areas with high intensity. This new method was evaluated in two publicly available databases, containing both normal and pathological images, and was able to obtain a valid location for the optic disc in 115 out of the 121 images of the two datasets.

This paper proposes a classification-based method for automating the segmentation of the region of interest (ROI) in digital images of chromatographic plates. Image segmentation is performed in two phases. In the first phase an unsupervised learning method classifies the image pixels into three classes: frame, ROI or unknown. In the second phase, distance features calculated for the members of the three classes are used for deciding on the new label, ROI or frame, for each individual connected segment previously classified as unknown.The segmentation result is post-processed using a sequence of morphological operators beforeobtaining the final ROI rectangular area. The proposed methodology, which is the initial step for the development of a screening tool for Fabry disease, was successfully evaluated in a dataset of 58 chromatographic images.

This paper presents a method for automating the selection of the rejection rate of one-class classifiers aiming at optimizing the classifier performance. These classifiers are used in a new classification approach to deal with class imbalance in Thin-Layer Chromatography (TLC) patterns, which is due to the huge difference between the number of normal and pathological cases, as a consequence of the rarity of Lysosomal Storage Disorders (LSD) diseases. The classification is performed in two decision stages, both implemented using optimized one-class classifiers: the first stage aims at recognizing most of the normal samples; the outliers of this first decision level are presented to the second stage, which is a multiclassifier prepared to deal with both pathological and normal patterns. The results that were obtained proved that the proposed methodology is able to overcome some of the difficulties associated with the choice of the rejection rate of one-class classifiers, and generally contribute to the minimization of the imbalance problem in TLC pattern classification.

This paper presents a new classification approach to deal with class imbalance in TLC patterns, which is due to the huge difference between the number of normal and pathological cases as a consequence of the rarity of LSD diseases. The proposed architecture is formed by two decision stages: the first is implemented by a one-class classifier aiming at recognizing most of the normal samples; the second stage is a hierarchical classifier which deals with the remaining outliers that are expected to contain the pathological cases and a small percentage of normal samples. We have also evaluated this architecture by a forest of classifiers, using the majority voting as a rule to generate the final classification. The results that were obtained proved that this approach is able to overcome some of the difficulties associated with class imbalance.

The paper presents the methodology developed to solve the class imbalanced problem that occurs in the classification of Thin-Layer Chromatography (TLC) images. The proposed methodology is based on re-sampling, and consists in the undersampling of the majority class (normal class), while the minority classes, which contain Lysosomal Storage Disorders (LSD) samples, are oversampled with the generation of synthetic samples. For image classification two approaches are presented, one based on a hierarchical classifier and another uses a multiclassifier system, where both classifiers are trained and tested using balanced data sets. The results demonstrate a better performance of the multiclassifier system using the balanced sets.

Thin-Layer Chromatography images are used to detect and identify the presence of specific oligosaccharides, expressed by the existence, at different positions, of bands in the gel image. 1D gaussian deconvolution, commonly used for band detection, does not produce good results due to the large curvature observed in the bands. To overcome this uncertainty on the band position, we propose a novel feature extraction methodology that allows an accurate modeling of curved bands. The features are used to classify the data into two different classes, to differentiate normal from pathologic cases. The paper presents the developed methodology together with the analysis and discussion of the results.

This work aims at developing an automatic method for the analysis of TLC images for measuring a set of features that can be used for the characterization of the distinctive patterns that result from the separation of oligosaccharides contained in human urine. This paper describes the methods developed for the automatic detection of the lanes contained in TLC images, and for the automatic separation of bands for each detected lane. The extraction of quantitative information related with each band was accomplished with two methods: the EM expectation-maximization and nonlinear least squares trust-region algorithms. The results of these methods, as well as additional quantitative information related with each band, are also presented.