Abstract:
Computed Tomography (CT) imaging plays a crucial role in diagnosing pelvic disorders such
as cancers, vascular abnormalities, and traumatic injuries. However, reducing radiation dose in pelvic CT
scans—a key priority under the ALARA (As Low As Reasonably Achievable) principle—often leads to in
creased noise and reduced contrast, impairing diagnostic accuracy. Addressing this trade-off, the present
research aims to minimize radiation exposure while maintaining diagnostic-quality imaging through a hy
brid image enhancement framework combining Non-Local Means (NLM) filtering for noise suppression and
wavelet-based transform techniques for contrast enhancement. A dataset of 140 anonymized low-dose pelvic
CT images from Thellipalai Base Hospital, Jaffna, was used. The proposed two-stage approach first applies
NLM denoising to reduce random noise while preserving fine structures, followed by wavelet decomposition
and thresholding to selectively enhance high-frequency details. A grid search across NLM smoothness pa
rameters (5–30) and wavelet thresholds (0.01–0.09) identified the optimal configuration (h = 5, threshold
= 0.01). Quantitative analysis using Peak Signal-to-Noise Ratio (PSNR) and Signal-to-Noise Ratio (SNR)
showed marked improvement over standalone enhancement methods, achieving mean PSNR above 44 dB and
substantially enhanced SNR values. These improvements indicate clearer structural delineation and reduced
visual fatigue, as confirmed by radiologist feedback. Compared with conventional single-stage methods, the
hybrid approach demonstrated superior balance between noise reduction and anatomical detail preservation,
directly supporting accurate diagnoses without additional radiation burden. This research highlights how
optimized hybrid enhancement can bridge the gap between dose reduction and image interpretability, offer
ing a scalable, low-cost solution for clinical settings—especially in low-resource environments. Future work
will integrate machine learning– based adaptive parameter tuning to further automate and personalize the
enhancement process for diverse diagnostic needs.
