eess.IVAug 27, 2026

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

Authors: Shahid-E-Kaiser Md. Tashrif, Munshi Md Arafat Hussain, Sheikh Nahian, Sumaiya Islam

Organizations: Institute of Information Technology, University of Dhaka, Dhaka 1000, Bangladesh

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Abstract

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.

Figures & tables

Figure 1: Phase 1 and Phase 2 of the proposed PST-CED + Fixed CLAHE pipeline, showing base morphological preprocessing and the proposed CLAHE enhancement branch.
Figure 2: Phase 3 of the proposed pipeline, showing sequential similarity-based Canny parameter tuning and final edge-map evaluation.
MetricBaseline ModelProposed Model (Opt. CLAHE)
Accuracy0.9269 ±\pm 0.00960.9230 ±\pm 0.0150
Precision0.5771 ±\pm 0.10020.5332 ±\pm 0.0869
Recall (Sensitivity)0.3327 ±\pm 0.07560.4636 ±\pm 0.0751
F1-Score0.4161 ±\pm 0.07260.4887 ±\pm 0.0584
Kappa Index0.3805 ±\pm 0.07180.4477 ±\pm 0.0599
SSIM0.6421 ±\pm 0.04420.6661 ±\pm 0.0493
Table 1: Performance Comparison: Baseline vs. Proposed Hybrid Model (Mean ±\pm Std over MRI images)
Figure 3: Qualitative comparison of edge detection results on representative MRI images. From left to right: original preprocessed image, vanilla PST-CED, and PST-CED with fixed CLAHE preprocessing. Each row represents a different MRI sample.

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