Dialectical Multispectral Classification of Diffusion-Weighted Magnetic Resonance Images as an Alternative to Apparent Diffusion Coefficients Maps to Perform Anatomical Analysis

TL;DR

This paper introduces the Objective Dialectical Method (ODM), inspired by philosophy, for classifying multispectral diffusion-weighted MRI images, enabling better differentiation of brain tissues and providing anatomical insights beyond traditional ADC maps.

Contribution

The paper presents a novel classification approach based on the Philosophy of Praxis, enhancing multispectral MRI analysis and tissue differentiation.

Findings

Gray and white matter can be distinguished using ODM-based multispectral analysis.

DW-MR images can provide anatomical information beyond ADC maps.

ODM outperforms traditional methods in tissue classification accuracy.

Abstract

Multispectral image analysis is a relatively promising field of research with applications in several areas, such as medical imaging and satellite monitoring. A considerable number of current methods of analysis are based on parametric statistics. Alternatively, some methods in Computational Intelligence are inspired by biology and other sciences. Here we claim that Philosophy can be also considered as a source of inspiration. This work proposes the Objective Dialectical Method (ODM): a method for classification based on the Philosophy of Praxis. ODM is instrumental in assembling evolvable mathematical tools to analyze multispectral images. In the case study described in this paper, multispectral images are composed of diffusion-weighted (DW) magnetic resonance (MR) images. The results are compared to ground-truth images produced by polynomial networks using a morphological similarity index. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map. Such results proved that gray and white matter can be distinguished in DW-MR multispectral analysis and, consequently, DW-MR images can also be used to furnish anatomical information.