Using the value of Lin’s concordance correlation coefficient as a criterion for efficient estimation of areas of leaves of eelgrass from noisy digital images

Abstract

BackgroundEelgrass is a cosmopolitan seagrass species that provides important ecological services in coastal and near-shore environments. Despite its relevance, loss of eelgrass habitats is noted worldwide. Restoration by replanting plays an important role, and accurate measurements of the standing crop and productivity of transplants are important for evaluating restoration of the ecological functions of natural populations. Traditional assessments are destructive, and although they do not harm natural populations, in transplants the destruction of shoots might cause undesirable alterations. Non-destructive assessments of the aforementioned variables are obtained through allometric proxies expressed in terms of measurements of the lengths or areas of leaves. Digital imagery could produce measurements of leaf attributes without the removal of shoots, but sediment attachments, damage infringed by drag forces or humidity contents induce noise-effects, reducing precision. Available techniques for dealing with noise caused by humidity contents on leaves use the concepts of adjacency, vicinity, connectivity and tolerance of similarity between pixels. Selection of an interval of tolerance of similarity for efficient measurements requires extended computational routines with tied statistical inferences making concomitant tasks complicated and time consuming. The present approach proposes a simplified and cost-effective alternative, and also a general tool aimed to deal with any sort of noise modifying eelgrass leaves images. Moreover, this selection criterion relies only on a single statistics; the calculation of the maximum value of the Concordance Correlation Coefficient for reproducibility of observed areas of leaves through proxies obtained from digital images.ResultsAvailable data reveals that the present method delivers simplified, consistent estimations of areas of eelgrass leaves taken from noisy digital images. Moreover, the proposed procedure is robust because both the optimal interval of tolerance of similarity and the reproducibility of observed leaf areas through digital image surrogates were independent of sample size.ConclusionThe present method provides simplified, unbiased and non-destructive measurements of eelgrass leaf area. These measurements, in conjunction with allometric methods, can predict the dynamics of eelgrass biomass and leaf growth through indirect techniques, reducing the destructive effect of sampling, fundamental to the evaluation of eelgrass restoration projects thereby contributing to the conservation of this important seagrass species.

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

“…[31][32][33][34][35] Other image and intelligent processing methods proposed in the literature, which are or can be successfully applied in the medical domain, are: echocardiograms analysis using the Fuzzy Gravitational Search algorithm to find the optimal architecture of the modular neural networks 36,37 ; electrocardiogram signals classification using competitive neural networks with the Learning Vector Quantization algorithm 38 ; cardiac arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks 39 ; and criterion for efficient estimation of areas in noisy digital images based on the usage of the Lin's concordance correlation coefficient. 40 In the last two decades, more NI algorithms were proposed without a rigorous mathematical analysis or justification, but they model nature's intelligence and thus they are able to solve certain optimization problems. In this article, the single-objective optimization in the continuous space is studied.…”