Multiple-Feature Construction for Image Segmentation Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8512

@Article{herrera-sanchez:2025:MaCA2,

• author = "David Herrera-Sanchez and Jose-Antonio Fuentes-Tomas and Hector-Gabriel Acosta-Mesa and Efren Mezura-Montes and Jose-Luis Morales-Reyes",
• title = "Multiple-Feature Construction for Image Segmentation Based on Genetic Programming",
• journal = "Mathematical and Computational Applications",
• year = "2025",
• volume = "30",
• number = "3",
• pages = "Article No. 57",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2297-8747",
• URL = "https://www.mdpi.com/2297-8747/30/3/57",
• DOI = "doi:10.3390/mca30030057",
• abstract = "Within the medical field, computer vision has an important role in different tasks, such as health anomaly detection, diagnosis, treatment, and monitoring medical conditions. Image segmentation is one of the most used techniques for medical support to identify regions of interest in different organs. However, performing accurate segmentation is difficult due to image variations. In this way, this work proposes an automated multiple-feature construction approach for image segmentation, working with magnetic resonance images, computed tomography, and RGB digital images. Genetic programming is used to automatically create and construct pipelines to extract meaningful features for segmentation tasks. Additionally, a co-evolution strategy is proposed within the evolution process to increase diversity without affecting segmentation performance. The segmentation is addressed as a pixel classification task; in this way, a wrapper approach is used, and the classification model's segmentation performance determines the fitness. To validate the effectiveness of the proposed method, four datasets were used to measure the capability of the proposal to deal with different types of medical images. The results demonstrate that the proposal achieves values of the DICE similarity coefficient of more than 0.6 in MRI and C.T. images. Additionally, the proposal is compared with SOTA GP-based methods and the convolutional neural networks used within the medical field. The method proposed outperforms these methods, achieving improvements greater than 20percent in DICE, specificity, and sensitivity. Additionally, the qualitative results demonstrate that the proposal accurately identifies the region of interest.",
• notes = "also known as \cite{mca30030057}",

}

Genetic Programming entries for David Herrera-Sanchez Jose-Antonio Fuentes-Tomas Hector-Gabriel Acosta-Mesa Efren Mezura-Montes Jose-Luis Morales-Reyes

Citations