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| Format: | Artículo científico |
| Language: | en |
| Published: |
Universidad Distrital Francisco José de Caldas
2024
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| Online Access: | https://www.redalyc.org/articulo.oa?id=498880092003 https://www.redalyc.org/journal/4988/498880092003/ https://www.redalyc.org/journal/4988/498880092003/html/ https://www.redalyc.org/journal/4988/498880092003/498880092003.epub https://www.redalyc.org/journal/4988/498880092003/movil |
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Table of Contents:
- Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images Azhar Albaaj Yaser Norouzi Gholamreza Moradi Ingeniería breast tumor microwave images threshold density logistic regression Automatic segmentation Context: Breast cancer remains a major health burden worldwide, necessitating improved screening modalities for early detection. However, existing techniques such as mammography and MRI exhibit limitations regarding sensitivity and specificity. Microwave imaging has recently emerged as a promising technology for breast cancer diagnosis, exploiting the dielectric contrast between normal and malignant tissues.Objectives: This study proposes a novel computational framework integrating thresholding, edge segmentation, and logistic regression to enhance microwave image-based breast tumor delineation.Methodology: The employed algorithm selects optimal features using logistic regression to mitigate the class imbalance between tumor and healthy tissues. Localized density thresholds are applied to identify tumor regions, followed by edge segmentation methods to precisely localize the detected lesions.Results: When evaluated on a dataset of microwave breast images, our approach demonstrated high accuracy for detecting and segmenting malignant tissues. Density thresholds ranging from 0.1 to 0.8 showcase the highest accuracy in detecting breast tumors from these images.Conclusions: The results highlight the potential of the proposed segmentation algorithm to improve the reliability of microwave imaging as an adjunct modality for breast cancer screening. This could promote earlier diagnosis and better clinical outcomes. The proposed framework represents a significant advance in developing robust image processing techniques tailored to emerging medical imaging modalities challenged by class imbalance and low intrinsic contrast. 2024 artículo científico 0121-750X https://www.redalyc.org/articulo.oa?id=498880092003 https://www.redalyc.org/journal/4988/498880092003/ https://www.redalyc.org/journal/4988/498880092003/html/ https://www.redalyc.org/journal/4988/498880092003/498880092003.epub https://www.redalyc.org/journal/4988/498880092003/movil 10.14483/23448393.20677 en http://www.redalyc.org/revista.oa?id=4988 Ingeniería application/pdf Universidad Distrital Francisco José de Caldas Ingeniería (Colombia) Num.2 Vol.29