Classification-based segmentation of the region of interest in chromatographic images

Abstract

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 before obtaining 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. © 2011 Springer-Verlag.