Robust Segmentation-Free Algorithm for Homogeneity Quantification in Images.

Area of Science:

• Image analysis
• Biomaterials science
• Materials science

Background:

• Homogeneity is crucial in various scientific fields but lacks a standardized definition and quantification method.
• Existing homogeneity quantification algorithms are often complex and lack robustness, leading to subjective analysis.
• This limits objective data comparison and hinders understanding of homogeneity's impact.

Purpose of the Study:

• To define homogeneity and propose a novel algorithm for its quantification.
• To address the lack of objective and robust methods for measuring image homogeneity.

Main Methods:

• Developed the Multi-scale Algorithm for Statistical QUantification of Homogeneity (MASQH).
• MASQH employs a multi-scale, statistical, and segmentation-free approach.
• The algorithm outputs a single, user-friendly homogeneity index.

Main Results:

• Validated MASQH on synthetic images across diverse scenarios.
• Demonstrated reliability on histological images of chitosan-platelet-rich-plasma biomaterials, comparing results with expert qualitative assessment.
• Evaluated MASQH performance against two existing algorithms using nanocomposite images.

Conclusions:

• The MASQH method offers a reliable and objective approach to quantify image homogeneity.
• This quantification facilitates inter-study comparisons and highlights homogeneity's significance in diverse scientific domains.
• The MASQH algorithm is freely accessible online.