ArticleJournal of digital imaging2020
Feature Enhancement in Medical Ultrasound Videos Using Contrast-Limited Adaptive Histogram Equalization.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 84 citations in OpenAlex.
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- Article
- Performance Comparison of U-Net and Its Variants for Carotid Intima-Media Segmentation in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2025Article
- Utilization of a Deep Learning Algorithm for Automated Segmentation of Median Nerve from Ultrasound Obtained from the Distal Forearm and Wrist.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Learning Model for Volume Measurement of the Remnant Pancreas After Pancreaticoduodenectomy and Distal Pancreatectomy.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing Conjunctival Vasculature Imaging: A Multi-Objective Cuckoo Search Approach for Contrast Enhancement Optimization.Translational vision science & technology · 2025Article
- Development and validation of a cranial ultrasound imaging-based deep learning model for periventricular-intraventricular haemorrhage detection and grading: a two-centre study.Pediatric radiology · 2025Observational
- GBCHV an advanced deep learning anatomy aware model for accurate classification of gallbladder cancer utilizing ultrasound images.Scientific reports · 2025Article
- DSTAN: A Deformable Spatial-temporal Attention Network with Bidirectional Sequence Feature Refinement for Speckle Noise Removal in Thyroid Ultrasound Video.Journal of imaging informatics in medicine · 2024Article
- Evaluation of histogram equalization and contrast limited adaptive histogram equalization effect on image quality and fractal dimensions of digital periapical radiographs.Oral radiology · 2023Article
- Automated pancreas segmentation and volumetry using deep neural network on computed tomography.Scientific reports · 2022Article
- Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast.Journal of biomedical optics · 2022Article
- Clinical Knowledge Mining Based on Image Enhancement Algorithm: Endoscopic Clinical Analysis of Peptic Ulcer in Children.Computational intelligence and neuroscience · 2022Article
- SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos.Health information science and systems · 2021Article
- Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic.Diagnostics (Basel, Switzerland) · 2021Review
- Classification of Chronic Kidney Disease in Sonography Using the GLCM and Artificial Neural Network.Diagnostics (Basel, Switzerland) · 2021Article
- COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data.IEEE access : practical innovations, open solutions · 2020Article
- A User-friendly Approach for the Diagnosis of Diabetic Retinopathy Using ChatGPT and Automated Machine Learning.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Speckle noise reduction algorithms are extensively used in the field of ultrasound image analysis with the aim of improving image quality and diagnostic accuracy. However, significant speckle filtering induces blurring, and this requires the enhancement of features and fine details. We propose a novel framework for both multiplicative noise suppression and robust contrast enhancement and demonstrate its effectiveness using a wide range of clinical ultrasound scans. Our approach to noise suppression uses a novel algorithm based on a convolutional neural network that is first trained on synthetically modeled ultrasound images and then applied on real ultrasound videos. The feature improvement stage uses an improved contrast-limited adaptive histogram equalization (CLAHE) method for enhancing texture features, contrast, resolvable details, and image structures to which the human visual system is sensitive in ultrasound video frames. The proposed CLAHE algorithm also considers an automatic system for evaluating the grid size using entropy, and three different target distribution functions (uniform, Rayleigh, and exponential), and interpolation techniques (B-spline, cubic, and Lanczos-3). An extensive comparative study has been performed to find the most suitable distribution and interpolation techniques and also the optimal clip limit for ultrasound video feature enhancement after speckle suppression. Subjective assessments by four radiologists and experimental validation using three quality metrics clearly indicate that the proposed framework generates superior performance compared with other well-established methods. The processing pipeline reduces speckle effectively while preserving essential information and enhancing the overall visual quality and therefore could find immediate applications in real-time ultrasound video segmentation and classification algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.