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Enhancing MRI Brain Tumor Edge Detection: A Hybrid Preprocessing Approach Utilizing CLAHE — arXiv:2608.28709

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28709
License (as archived): CC0-1.0
Archived: 2026-10-17T09:59:56.010Z
SHA-256 of the archived text: 5bc057f92a8480c4ad4df254965c47e9e1490695a18dbe3ed81d53eb61daf18d
Stable URL of this page: https://metraipolis.com/documento/dc_dd092fcaf46e

How to cite

Enhancing MRI Brain Tumor Edge Detection: A Hybrid Preprocessing Approach Utilizing CLAHE — arXiv:2608.28709 — arXiv (en), https://arxiv.org/abs/2608.28709 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dd092fcaf46e (Metraipolis archive dc_dd092fcaf46e, sha256 5bc057f92a8480c4ad4df254965c47e9e1490695a18dbe3ed81d53eb61daf18d)

BibTeX

@misc{dcdd092fcaf46e,
  title = {Enhancing MRI Brain Tumor Edge Detection: A Hybrid Preprocessing Approach Utilizing CLAHE — arXiv:2608.28709},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28709},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dd092fcaf46e; sha256 5bc057f92a8480c4ad4df254965c47e9e1490695a18dbe3ed81d53eb61daf18d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28709. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate boundary delineation of brain tumors in Magnetic Resonance Imaging (MRI) is a critical yet formidable challenge in neuro-oncology due to inherent scanner noise, complex anatomical structures, and uneven illumination. Traditional edge detection algorithms, while computationally lightweight and mathematically interpretable, frequently fail to capture the diffuse, localized boundaries of edema when relying solely on global preprocessing and manual parameter tuning. To overcome these limitations, we propose a hybrid automated edge detection pipeline. Our approach integrates an optimally configured Contrast-Limited Adaptive Histogram Equalization (CLAHE) layer into a comprehensive morphological preprocessing framework, followed by a deterministic sequential parameter sweep to fully automate threshold selection. The proposed hybrid model demonstrated enhancement in detecting critical anatomical structures in a publicly available benchmark database from Kaggle. By intelligently amplifying localized gradients without overwhelming the image with background noise, our method achieved higher Recall (Sensitivity). Consequently, the overall F1-Score elevated, and the Structural Similarity Index (SSIM) improved, all while maintaining a highly efficient execution. This establishes our optimized pipeline as a highly practical and near real-time operational model for clinical diagnostics, offering a compelling alternative to computationally heavy deep learning approaches.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in cs.CV).
Categorie arXiv: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.28709 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T09:28:48.239Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).