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Breast cancer (BC) remains a predominant concern among women globally. Striving to prevent and identify BC in its initial phases, the development of computer aided-diagnosis (CAD) is imperative. These systems play a pivotal role in precisely controlling tumor growth and administering tailored treatments based on the tumor’s pathological stage. The foundational step in creating such a system involves a crucial pretreatment phase aimed at enhancing the image boundaries and structures quality. Subsequently, the segmentation step becomes essential, particularly in the context of Medio-Lateral-Oblique (MLO) view mammograms, where the images encompass the pectoral muscle (PM) situated in the upper corner. This paper introduces a novel approach for PM removal in MLO mammogram observations, leveraging region, and edge-based concepts. The suggested method has been rigorously evaluated using digital mammography from the Mini-MIAS database, through the DICE Coefficient, Structural Similarity (SSIM) and Jaccard Similarty Index (JSI) metrics, providing insights into the segmentation quality against the ground truth. The findings affirm the effectiveness of the suggested approach in comparison to several other methods within the identical field.

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Toward an Enhanced Approach for Pectoral Muscle Segmentation in MLO View Mammograms

  • 会议论文

  • 发表日期：

    2026年

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  • Nourane Laaffat,
  • Ahmad Outfarouin,
  • Walid Bouarifi,
  • Abdelilah Jraifi

摘要

Breast cancer (BC) remains a predominant concern among women globally. Striving to prevent and identify BC in its initial phases, the development of computer aided-diagnosis (CAD) is imperative. These systems play a pivotal role in precisely controlling tumor growth and administering tailored treatments based on the tumor’s pathological stage. The foundational step in creating such a system involves a crucial pretreatment phase aimed at enhancing the image boundaries and structures quality. Subsequently, the segmentation step becomes essential, particularly in the context of Medio-Lateral-Oblique (MLO) view mammograms, where the images encompass the pectoral muscle (PM) situated in the upper corner. This paper introduces a novel approach for PM removal in MLO mammogram observations, leveraging region, and edge-based concepts. The suggested method has been rigorously evaluated using digital mammography from the Mini-MIAS database, through the DICE Coefficient, Structural Similarity (SSIM) and Jaccard Similarty Index (JSI) metrics, providing insights into the segmentation quality against the ground truth. The findings affirm the effectiveness of the suggested approach in comparison to several other methods within the identical field.