Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification

Abstract

Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions (MFuzzyEn2D and MDispEn2D, respectively) have shown promising results for image classifications. However, these results rely on the selection of key parameters that may largely influence the entropy values obtained. Yet, the optimal choice for these parameters has not been studied thoroughly. We propose a study on the impact of these parameters in image classification. For this purpose, the entropy-based algorithms are applied to a variety of images from different datasets, each containing multiple image classes. Several parameter combinations are used to obtain the entropy values. These entropy values are then applied to a range of machine learning classifiers and the algorithm parameters are analyzed based on the classification results. By using specific parameters, we show that both MFuzzyEn2D and MDispEn2D approach state-of-the-art in terms of image classification for multiple image types. They lead to an average maximum accuracy of more than 95% for all the datasets tested. Moreover, MFuzzyEn2D results in a better classification performance than that extracted by MDispEn2D as a majority. Furthermore, the choice of classifier does not have a significant impact on the classification of the extracted features by both entropy algorithms. The results open new perspectives for these entropy-based measures in textural analysis.

Colored Texture Analysis Fuzzy Entropy Methods with a Dermoscopic Application

“…We can also observe that for the RGB color space, the CBT images that are perceived visually to be of higher color and pattern irregularity, Figure 4 c,f,g, obtained higher entropy values than the others, whereas those that appear to be of periodic well-defined repetitive patterns, Figure 4 a,b,e, resulted in lower entropy values for the three measures , , and . This is in accordance with the literature of entropy measures and information theory concept applied to gray level texture images [ 12 , 14 , 15 , 16 , 17 , 18 , 38 ].…”

“…We recently developed bidimensional fuzzy entropy, , and its multi-scale extension [ 17 , 18 , 38 ]. These entropy measures revealed interesting results for some dermoscopic images but were limited to gray scale images.…”

“…The latter ensures calculating a participation degree for all the compared patterns and quantifies the irregularity of the analyzed data. This information theory concept has been proven to be reliable for 1D, 2D, and 3D data [ 17 , 18 , 38 , 39 , 40 ]. However, only gray scale data have been investigated to date.…”

“…The latter two classes (learning-based approaches and entropy-based approaches) are the most recent ones. Several studies have shown that the entropy-based measures are promising for texture analysis [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. However, these studies are only at their beginning.…”

“…This depends on many factors, such as the need to analyze texture or color, separately or combined, directly from the image or through a transformation, among other factors [ 19 , 24 , 25 , 26 ]. Only a few studies have been performed on colored texture analysis and most of them were achieved by adapting the application of gray scale textures analysis methods [ 13 , 18 , 27 , 28 ]. Nevertheless, color and texture are probably the most important components of visual features.…”