Image analysis for automatic measurement of crustose lichens

TL;DR

This paper introduces an automated image analysis workflow for measuring crustose lichens, significantly reducing manual effort and increasing accuracy in size estimation on rocky surfaces.

Contribution

It develops a novel workflow combining image acquisition, correction, segmentation, and classification tools for efficient lichen measurement.

Findings

Manual classification speed increased by 4 times with 95% precision

Automatic classification achieves over 70% precision

Workflow reduces processing time and is adaptable to new datasets

Abstract

Lichens, organisms resulting from a symbiosis between a fungus and an algae, are frequently used as age estimators, especially in recent geological deposits and archaeological structures, using the correlation between lichen size and age. Current non-automated manual lichen and measurement (with ruler, calipers or using digital image processing tools) is a time-consuming and laborious process, especially when the number of samples is high. This work presents a workflow and set of image acquisition and processing tools developed to efficiently identify lichen thalli in flat rocky surfaces, and to produce relevant lichen size statistics (percentage cover, number of thalli, their area and perimeter). The developed workflow uses a regular digital camera for image capture along with specially designed targets to allow for automatic image correction and scale assignment. After this step, lichen identification is done in a flow comprising assisted image segmentation and classification based on interactive foreground extraction tool (GrabCut) and automatic classification of images using Simple Linear Iterative Clustering (SLIC) for image segmentation and Support Vector Machines (SV) and Random Forest classifiers. Initial evaluation shows promising results. The manual classification of images (for training) using GrabCut show an average speedup of 4 if compared with currently used techniques and presents an average precision of 95\%. The automatic classification using SLIC and SVM with default parameters produces results with average precision higher than 70\%. The developed system is flexible and allows a considerable reduction of processing time, the workflow allows it applicability to data sets of new lichen populations.