Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity

Espinosa, Ricardo ; Bailón, Raquel (Universidad de Zaragoza) ; Laguna, Pablo (Universidad de Zaragoza)

Resumen: Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7.

Idioma: Inglés

DOI: 10.3390/e23101261

Año: 2021

Publicado en: ENTROPY 23, 10 (2021), e23101261
ISSN: 1099-4300

Factor impacto JCR: 2.738 (2021)

Categ. JCR: PHYSICS, MULTIDISCIPLINARY rank: 42 / 86 = 0.488 (2021) - Q2 - T2

Factor impacto CITESCORE: 4.4 - Physics and Astronomy (Q2)

Factor impacto SCIMAGO: 0.553 - Electrical and Electronic Engineering (Q2) - Physics and Astronomy (miscellaneous) (Q2) - Information Systems (Q2)

Tipo y forma: Article (Published version)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)