DOI:

https://doi.org/10.14483/23448393.20677

Published:

2024-05-22

Issue:

Vol. 29 No. 2 (2024): May-August

Section:

Electrical, Electronic and Telecommunications Engineering

Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images

Detección y segmentación automatizadas de tumores de mama mediante el algoritmo de densidad de umbral con regresión logística en imágenes por microondas

Authors

Keywords:

Automatic Segmentation, Breast Tumor, Logistic Regression, Microwave Images, Threshold Density (en).

Keywords:

segmentación automática, tumor de mama, regresión logística, imágenes por microondas, densidad de umbral (es).

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Abstract (en)

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.

Abstract (es)

Contexto: El cáncer de mama sigue siendo una importante carga sanitaria a nivel mundial, lo que requiere mejores modalidades de cribado para la detección temprana. Sin embargo, las técnicas existentes, como la mamografía y la resonancia magnética, presentan limitaciones en cuanto a sensibilidad y especificidad. Recientemente, la imagen por microondas ha surgido como una prometedora tecnología para el diagnóstico del cáncer de mama, aprovechando el contraste dieléctrico entre los tejidos normales y malignos. Objetivos: Este estudio propone un novedoso marco computacional que integra el umbralizado, la segmentación de bordes y la regresión logística para mejorar la delimitación de tumores mamarios basada en imágenes de microondas. Metodología: El algoritmo empleado selecciona las características óptimas utilizando la regresión logística para mitigar el desequilibrio de clases entre los tejidos tumorales y sanos. Se aplican umbrales de densidad localizados para identificar las regiones tumorales, seguidos de métodos de segmentación de bordes para localizar precisamente las lesiones detectadas. Resultados: Cuando se evaluó en un conjunto de datos de imágenes de microondas de mama, nuestro enfoque demostró una alta precisión para detectar y segmentar los tejidos malignos. Los umbrales de densidad que van desde 0,1 hasta 0,8 muestran la mayor precisión en la detección de tumores mamarios a partir de estas imágenes. Conclusiones: Los resultados resaltan el potencial del algoritmo de segmentación propuesto para mejorar la fiabilidad de la imagen por microondas como modalidad complementaria para el cribado del cáncer de mama. Esto podría promover un diagnóstico más temprano y mejores resultados clínicos. El marco propuesto representa un avance significativo en el desarrollo de técnicas robustas de procesamiento de imágenes adaptadas a las modalidades emergentes de imagen médica desafiadas por el desequilibrio de clases y el bajo contraste intrínseco.

Author Biography

Gholamreza Moradi , Amirkabir University of Technology

Ministry of Health in Iraq, Najaf Health Directorate, Al-Hakim General Hospital, Najaf, Iraq.

References

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How to Cite

APA

Albaaj, A., Norouzi, Y., and Moradi , G. (2024). Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images. Ingeniería, 29(2), e20677. https://doi.org/10.14483/23448393.20677

ACM

[1]
Albaaj, A. et al. 2024. Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images. Ingeniería. 29, 2 (May 2024), e20677. DOI:https://doi.org/10.14483/23448393.20677.

ACS

(1)
Albaaj, A.; Norouzi, Y.; Moradi , G. Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images. Ing. 2024, 29, e20677.

ABNT

ALBAAJ, Azhar; NOROUZI, Yaser; MORADI , Gholamreza. Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images. Ingeniería, [S. l.], v. 29, n. 2, p. e20677, 2024. DOI: 10.14483/23448393.20677. Disponível em: https://revistas.udistrital.edu.co/index.php/reving/article/view/20677. Acesso em: 19 jul. 2024.

Chicago

Albaaj, Azhar, Yaser Norouzi, and Gholamreza Moradi. 2024. “Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images”. Ingeniería 29 (2):e20677. https://doi.org/10.14483/23448393.20677.

Harvard

Albaaj, A., Norouzi, Y. and Moradi , G. (2024) “Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images”, Ingeniería, 29(2), p. e20677. doi: 10.14483/23448393.20677.

IEEE

[1]
A. Albaaj, Y. Norouzi, and G. Moradi, “Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images”, Ing., vol. 29, no. 2, p. e20677, May 2024.

MLA

Albaaj, Azhar, et al. “Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images”. Ingeniería, vol. 29, no. 2, May 2024, p. e20677, doi:10.14483/23448393.20677.

Turabian

Albaaj, Azhar, Yaser Norouzi, and Gholamreza Moradi. “Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images”. Ingeniería 29, no. 2 (May 22, 2024): e20677. Accessed July 19, 2024. https://revistas.udistrital.edu.co/index.php/reving/article/view/20677.

Vancouver

1.
Albaaj A, Norouzi Y, Moradi G. Automated Breast Tumor Detection and Segmentation Using the Threshold Density Algorithm with Logistic Regression on Microwave Images. Ing. [Internet]. 2024 May 22 [cited 2024 Jul. 19];29(2):e20677. Available from: https://revistas.udistrital.edu.co/index.php/reving/article/view/20677

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