Biomedical images are often referenced for clinical decision support (CDS), educational purposes, and research. The
task of automatically finding the images in a scientific article that are most useful for the purpose of determining
relevance to a clinical situation is traditionally done using text and is quite challenging. We propose to improve this
by associating image features from the entire image and from relevant regions of interest with biomedical concepts
described in the figure caption or discussion in the article. However, images used in scientific article figures are
often composed of multiple panels where each sub-figure (panel) is referenced in the caption using alphanumeric
labels, e.g. Figure 1(a), 2(c), etc. It is necessary to separate individual panels from a multi-panel figure as a first step
toward automatic annotation of images.
In this work we present methods that add make robust our previous efforts reported here. Specifically, we address
the limitation in segmenting figures that do not exhibit explicit inter-panel boundaries, e.g. illustrations, graphs, and
charts. We present a novel hybrid clustering algorithm based on particle swarm optimization (PSO) with fuzzy logic
controller (FLC) to locate related figure components in such images.
Results from our evaluation are very promising with 93.64% panel detection accuracy for regular (non-illustration)
figure images and 92.1% accuracy for illustration images. A computational complexity analysis also shows that PSO
is an optimal approach with relatively low computation time. The accuracy of separating these two type images is
98.11% and is achieved using decision tree.