Haralick Textures Features as an Identifier of Triple Negative Breast Cancer
Year: 2025
Type: conference-paper
Abstract: This study aims to discern and analyze statistical differences in Haralick textures features in mammography images, specifically comparing patients diagnosed with triple-negative breast cancer with those with benign tumors. Methods: This study included two data sets (triple-negative breast cancer and benign tumor), each with 29 mammography images. Haralick textures features were extracted using MATLAB software (MathWorks, Natick, MA, USA). The Kolmogorov-Smirnov test was applied to verify the normality of the data distribution. Given that the data did not follow a normal distribution, the nonparametric Kruskal-Wallis test was used in the statistical analysis. Results: The texture characteristics that showed significant discriminatory power between the analyzed groups were: Contrast, Entropy, Sum of Entropy, Difference Entropy, and Informational Correlation Measure 2. Conclusion: The findings of this study support the adoption of a broad and iterative approach to texture analysis, involving the integration of relevant descriptors with complementary analytical techniques, aiming to improve the ability to differentiate between distinct clinical conditions.
Authors: Silva, Lucas de Brito, Pereira Patrocínio do Reis, Luigi, Andrade Correia, Luciene Narayama, Cunha Carneiro, Pedro, Patrocínio, Ana Claudia
Institution: Universidade Federal de Uberlândia
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7092551646
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