Open Access

ARTICLE

A Computational Model for Enhanced Mammographic Image Pre-Processing and Segmentation

Khlood M. Mehdar¹, Toufique A. Soomro^2,3,*, Ahmed Ali⁴, Faisal Bin Ubaid⁵, Muhammad Irfan^6,*, Sabah Elshafie Mohammed Elshafie¹, Aisha M. Mashraqi⁷, Abdullah A. Asiri⁸, Nagla Hussien Mohamed Khalid⁸, Hanan T. Halawani⁷

1 Department of Anatomy, Faculty of Medicine, Najran University, Najran, 61441, Saudi Arabia
2 Artificial Intelligence and Cyber Futures Institute, Charles University, Bathurst, NSW 2795, Australia
3 Department of Electronic Engineering, The University of Larkano, Larkana, Sindh, 75660, Pakistan
4 Eletrical Engineering Department, Sukkur IBA University, Sukkur, Sindh, 65200, Pakistan
5 Computer Science Department, Sukkur IBA University, Sukkur, Sindh, 65200, Pakistan
6 Electrical Engineering Department, College of Engineering, Najran University, Najran, 61441, Saudi Arabia
7 Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
8 Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, 61441, Saudi Arabia

* Corresponding Authors: Toufique A. Soomro. Email: ; Muhammad Irfan. Email:

Computer Modeling in Engineering & Sciences 2025, 143(3), 3091-3132. https://doi.org/10.32604/cmes.2025.065471

Received 13 March 2025; Accepted 03 June 2025; Issue published 30 June 2025

Abstract

Breast cancer remains one of the most pressing global health concerns, and early detection plays a crucial role in improving survival rates. Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities. However, existing methodologies face persistent challenges, including low image contrast, noise interference, and inaccuracies in segmenting regions of interest. To address these limitations, this study introduces a novel computational framework for analyzing mammographic images, evaluated using the Mammographic Image Analysis Society (MIAS) dataset comprising 322 samples. The proposed methodology follows a structured three-stage approach. Initially, mammographic scans are classified using the Breast Imaging Reporting and Data System (BI-RADS), ensuring systematic and standardized image analysis. Next, the pectoral muscle, which can interfere with accurate segmentation, is effectively removed to refine the region of interest (ROI). The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis (ICA) to enhance contrast, suppress noise, and improve image clarity. Following these enhancements, a robust segmentation technique is employed to delineated abnormal regions. Experimental results validate the efficiency of the proposed framework, demonstrating a significant improvement in the Effective Measure of Enhancement (EME) and a 3 dB increase in Peak Signal-to-Noise Ratio (PSNR), indicating superior image quality. The model also achieves an accuracy of approximately 97%, surpassing contemporary techniques evaluated on the MIAS dataset. Furthermore, its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications. This study presents an advanced and dependable computational framework for mammographic image analysis, effectively addressing critical challenges in noise reduction, contrast enhancement, and segmentation precision. The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic (CAD) systems, with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.

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Keywords

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