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<p>Traumatic Brain Injury, which is also known as TBI, is a serious global health concern, with neuroimaging playing a vital role in its diagnosis and prognosis. Skull stripping refers to the technique of isolating the brain region by eliminating non-brain structures from neuroimaging data. It is an essential preprocessing step in neuroimaging analysis to improve lesion detection, feature extraction, and tissue classification. This study presents an efficient skull stripping method, ThresContCT_TBI, specifically designed for computed tomography (CT) images of TBI patients. ThresContCT_TBI integrates intensity thresholding, morphological processing, contour filtering, and distance transforms to achieve robust brain extraction while addressing challenges such as traumatic lesions and anatomical complexities. The effectiveness of the approach was evaluated using segmentation metrics, including accuracy (98.68%), Dice coefficient (97.79%), intersection over union (IoU) (95.68%), and Hausdorff distance (HD). The results demonstrate the method’s effectiveness in preserving brain structures while accurately eliminating non-brain tissues. Compared to deep learning-based and traditional methods, our approach offers computational efficiency and does not require extensive labeled datasets, which makes it ideal for use in real-time applications in healthcare. This research emphasizes the critical role of optimized skull stripping in improving automated neuroimaging workflows, with potential applications in TBI diagnosis and beyond.</p>

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Automatic Skull Stripping for CT Images of Traumatic Brain Injuries (TBI)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Minhazur Rahman,
  • Bikash Upadhyay,
  • Rosy Sarmah

摘要

Traumatic Brain Injury, which is also known as TBI, is a serious global health concern, with neuroimaging playing a vital role in its diagnosis and prognosis. Skull stripping refers to the technique of isolating the brain region by eliminating non-brain structures from neuroimaging data. It is an essential preprocessing step in neuroimaging analysis to improve lesion detection, feature extraction, and tissue classification. This study presents an efficient skull stripping method, ThresContCT_TBI, specifically designed for computed tomography (CT) images of TBI patients. ThresContCT_TBI integrates intensity thresholding, morphological processing, contour filtering, and distance transforms to achieve robust brain extraction while addressing challenges such as traumatic lesions and anatomical complexities. The effectiveness of the approach was evaluated using segmentation metrics, including accuracy (98.68%), Dice coefficient (97.79%), intersection over union (IoU) (95.68%), and Hausdorff distance (HD). The results demonstrate the method’s effectiveness in preserving brain structures while accurately eliminating non-brain tissues. Compared to deep learning-based and traditional methods, our approach offers computational efficiency and does not require extensive labeled datasets, which makes it ideal for use in real-time applications in healthcare. This research emphasizes the critical role of optimized skull stripping in improving automated neuroimaging workflows, with potential applications in TBI diagnosis and beyond.