Automatic lane segmentation in TLC images using the continuous wavelet transform

Evaluation of semi-automatic image analysis tools for cerebrospinal fluid electrophoresis of IgG oligoclonal bands

Background

IgG concentrations in cerebrospinal fluid generally range from 20 to 45 mg/L. In multiple sclerosis immune reactions lead to intrathecal synthesis of specific IgGs that can be detected in biological fluid samples both quantitatively and qualitatively by isoelectric focusing of supplementary oligoclonal IgG bands.

Method

A simple tool, using the MATLAB application, to facilitate and improve isoelectric focusing profile analysis is presented and evaluated in terms of its sensitivity, repeatability and reproducibility. A comparison between human readers and semi-automatic method has also been performed.

Results

Results from the semi-automatic method were found to be equivalent or superior to generally employed laboratory methods. Repeatability analysis for semi-automatic processing yielded coefficients of variation (CVs) in the 3–7% range, and using a sample with an estimated IgG concentration of 200 mg/L, four bands were still visible after dilution to 5 mg/L, corresponding to band concentrations of 1.1–1.6 mg/L. Discordances between visual inspection and automatic analysis only appear at threshold levels for interpretation (the gray zone).

Conclusion

The semi-automatic method has acceptable performance for routine implementation.

Semi-automated image analysis of gel electrophoresis of cerebrospinal fluid for oligoclonal band detection

Detection of oligoclonal electrophoretic bands in cerebrospinal fluid (CSF) is an important diagnostic tool for Multiple Sclerosis (MS). Electrophoretic profiles are difficult to interpret due to low contrast and artefacts. A semi-automated method to ease analysis and to reduce subjectivity is presented. The method sequentially converts color images to grayscale, realigns bands, removes artifacts, then converts 2D images to a signal, before detecting, thresholding and editing peaks to optimize profiles. Such treated profiles (21 positive and 15 negative) are compared to ground truth analysis of an expert biologist. 16 profiles over 21 are well detected positive and 12 profiles over 15 are detected negative, results seem similar to inter-experts variability reported in literature.

Surveying and benchmarking techniques to analyse DNA gel fingerprint images

DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. The analysis of DNA gel fingerprint images usually consists of five consecutive steps: image pre-processing, lane segmentation, band detection, normalization and fingerprint comparison. In this article, we firstly survey the main methods that have been applied in the literature in each of these stages. Secondly, we focus on lane-segmentation and band-detection algorithms-as they are the steps that usually require user-intervention-and detect the seven core algorithms used for both tasks. Subsequently, we present a benchmark that includes a data set of images, the gold standards associated with those images and the tools to measure the performance of lane-segmentation and band-detection algorithms. Finally, we implement the core algorithms used both for lane segmentation and band detection, and evaluate their performance using our benchmark. As a conclusion of that study, we obtain that the average profile algorithm is the best starting point for lane segmentation and band detection.