ArticleJournal of digital imaging2013
Characterization of primary and secondary malignant liver lesions from B-mode ultrasound.
Article in Journal of digital imaging, 2013. 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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Who cites it
19 citing papers in PubMed, 76 citations in OpenAlex.
- Labeled Numerical Phantom of Abdominal Wall for Wave-Physics-Based Ultrasound Imaging: Applications to Image Reconstruction.IEEE transactions on ultrasonics · 2026Article
- Hepatic Involvement in Acquired Immunodeficiency Syndrome-Associated Kaposi's Sarcoma: A Descriptive Analysis on CT, MRI, and Ultrasound.Infection and drug resistance · 2024Article
- Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.Journal of medical systems · 2023Review
- [Ultrasound diagnostics of the liver : Principles and important pathologies].Radiologie (Heidelberg, Germany) · 2023Article
- Quantifying the Impact of Imaging Through Body Walls on Shear Wave Elasticity Measurements.Ultrasound in medicine & biology · 2023Article
- Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses.Bioengineering (Basel, Switzerland) · 2023Article
- Extent of use of artificial intelligence & machine learning protocols in cancer diagnosis: A scoping review.The Indian journal of medical research · 2023Article
- Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review.Journal of clinical medicine · 2022Review
- Ultrasound-based artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2022Review
- Quantifying the Effect of Abdominal Body Wall on In Situ Peak Rarefaction Pressure During Diagnostic Ultrasound Imaging.Ultrasound in medicine & biology · 2021Article
- Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis.Frontiers in oncology · 2021Review
- Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor.Frontiers in oncology · 2020Review
- Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology.Hepatology international · 2019Review
- Evaluation of Large-Aperture Imaging Through the ex Vivo Human Abdominal Wall.Ultrasound in medicine & biology · 2018Article
- Quantifying Image Quality Improvement Using Elevated Acoustic Output in B-Mode Harmonic Imaging.Ultrasound in medicine & biology · 2017Article
- Functional imaging of hepatocellular carcinoma.Hepatic oncology · 2016Review
- Quantitative sonographic image analysis for hepatic nodules: a pilot study.Journal of medical ultrasonics (2001) · 2015Article
- SVM-Based CAC System for B-Mode Kidney Ultrasound Images.Journal of digital imaging · 2015Article
- Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound.Journal of digital imaging · 2014Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterization of hepatocellular carcinomas (HCCs) and metastatic carcinomas (METs) from B-mode ultrasound presents a daunting challenge for radiologists due to their highly overlapping appearances. The differential diagnosis between HCCs and METs is often carried out by observing the texture of regions inside the lesion and the texture of background liver on which the lesion has evolved. The present study investigates the contribution made by texture patterns of regions inside and outside of the lesions for binary classification between HCC and MET lesions. The study is performed on 51 real ultrasound liver images with 54 malignant lesions, i.e., 27 images with 27 solitary HCCs (13 small HCCs and 14 large HCCs) and 24 images with 27 MET lesions (12 typical cases and 15 atypical cases). A total of 120 within-lesion regions of interest and 54 surrounding lesion regions of interest are cropped from 54 lesions. Subsequently, 112 texture features (56 texture features and 56 texture ratio features) are computed by statistical, spectral, and spatial filtering based texture features extraction methods. A two-step methodology is used for feature set optimization, i.e., feature pruning by removal of nondiscriminatory features followed by feature selection by genetic algorithm-support vector machine (SVM) approach. The SVM classifier is designed based on optimum features. The proposed computer-aided diagnostic system achieved the overall classification accuracy of 91.6 % with sensitivity of 90 % and 93.3 % for HCCs and METs, respectively. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in diagnosing liver malignancies.
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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.