Robust Segmentation-Free Algorithm for Homogeneity Quantification in Images.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Fiona MilanoMarc Lavertu

Abstract

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. 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. 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...Continue Reading

Software Mentioned

GreyScale
R
MASQH

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