Robust Segmentation-Free Algorithm for Homogeneity Quantification in Images

Objective: Homogeneity is a notion used to describe images in various fields and is often linked to critical aspects of those fields. However, this term is rarely defined in the literature and no gold standard exists for its quantification. A few quantification algorithms have been proposed, but they lack both simplicity and robustness. As a result, the scientific community uses the notion of homogeneity in subjective analysis, preventing objective comparison of a large number of data or of different studies. The main objectives of this manuscript are to propose a definition of homogeneity and an algorithm for its quantification. Method: This algorithm, called MASQH, rely on a multi-scale, statistical and segmentation-free approach and outputs a single homogeneity index, which makes it robust and easy to use. Results: The performance and reliability of the method are demonstrated through three case studies: Firstly, on synthetic images to study the behavior and assess the relevance of the algorithm in diverse situations and hence, in various potential fields. Secondly, on histological images derived from experimental chitosan-platelet-rich-plasma hybrid biomaterial, where the quantitative results are compared to a qualitative classification provided by an expert in the field. Thirdly, on experimental nanocomposites images for which results are compared to two other homogeneity quantification algorithms from the field of nanocomposites. Conclusion and significance: By quantifying homogeneity, the MASQH method may help to compare disparate studies in the literature and quantitatively demonstrate the impact of homogeneity in various fields. The MASQH method is freely available online.