Challenges, Advances, and Evaluation Metrics in Medical Image Enhancement: A Systematic Literature Review

2025-10-15

AI Summary

Medical image enhancement faces persistent challenges—including noise, artifacts, and low contrast—while suffering from modality imbalance: research is concentrated on MRI and multimodal imaging, whereas domain-specific modalities (e.g., histopathology, endoscopy, bone scintigraphy) remain critically underexplored. This review systematically evaluates the applicability and limitations of 65 image quality assessment (IQA) metrics and comparatively analyzes performance across 39 studies—29 classical, 9 deep learning–based, and 1 hybrid method—focusing on denoising, contrast enhancement, and detail preservation, validated via both reference-based and no-reference IQA metrics. Key contributions include: (1) identifying low contrast and noise as the most prevalent degradation factors; (2) exposing significant gaps in enhancement methods tailored to specialized clinical modalities; and (3) advocating for a more standardized, multi-metric, synergistic evaluation framework for medical image enhancement.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Medical image enhancement is crucial for improving the quality and interpretability of diagnostic images, ultimately supporting early detection, accurate diagnosis, and effective treatment planning. Despite advancements in imaging technologies such as X-ray, CT, MRI, and ultrasound, medical images often suffer from challenges like noise, artifacts, and low contrast, which limit their diagnostic potential. Addressing these challenges requires robust preprocessing, denoising algorithms, and advanced enhancement methods, with deep learning techniques playing an increasingly significant role. This systematic literature review, following the PRISMA approach, investigates the key challenges, recent advancements, and evaluation metrics in medical image enhancement. By analyzing findings from 39 peer-reviewed studies, this review provides insights into the effectiveness of various enhancement methods across different imaging modalities and the importance of evaluation metrics in assessing their impact. Key issues like low contrast and noise are identified as the most frequent, with MRI and multi-modal imaging receiving the most attention, while specialized modalities such as histopathology, endoscopy, and bone scintigraphy remain underexplored. Out of the 39 studies, 29 utilize conventional mathematical methods, 9 focus on deep learning techniques, and 1 explores a hybrid approach. In terms of image quality assessment, 18 studies employ both reference-based and non-reference-based metrics, 9 rely solely on reference-based metrics, and 12 use only non-reference-based metrics, with a total of 65 IQA metrics introduced, predominantly non-reference-based. This review highlights current limitations, research gaps, and potential future directions for advancing medical image enhancement.

Problem

• Addressing noise and low contrast challenges in medical diagnostic images
• Evaluating enhancement methods across different medical imaging modalities
• Identifying research gaps in specialized medical imaging enhancement techniques

Innovation

• Systematic review using PRISMA methodology
• Analyzing 39 studies on enhancement techniques
• Comparing conventional and deep learning approaches