ArticleJournal of digital imaging2013
Thyroid nodule recognition based on feature selection and pixel classification methods.
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 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning based thyroid tumor characterisation using ultrasonographic images.Journal of ultrasound · 2024Pooled it
- Identification of lesion location and discrimination between benign and malignant findings in thyroid ultrasound imaging.Scientific reports · 2024Article
- A Novel Distant Domain Transfer Learning Framework for Thyroid Image Classification.Neural processing letters · 2022Article
- Research on Classification Method of Medical Ultrasound Image Processing Based on Neural Network.Computational intelligence and neuroscience · 2022Article
- The Diagnostic Value of Artificial Intelligence Ultrasound S-Detect Technology for Thyroid Nodules.Computational intelligence and neuroscience · 2022Article
- Thyroid nodule recognition using a joint convolutional neural network with information fusion of ultrasound images and radiofrequency data.European radiology · 2021Article
- Article
- Update on thyroid ultrasound: a narrative review from diagnostic criteria to artificial intelligence techniques.Chinese medical journal · 2019Article
- Performance Evaluations on Using Entropy of Ultrasound Log-Compressed Envelope Images for Hepatic Steatosis Assessment: An In Vivo Animal Study.Entropy (Basel, Switzerland) · 2018Article
- MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer - A First Preliminary Study.Translational oncology · 2017Article
- Multi-institutional validation of a novel textural analysis tool for preoperative stratification of suspected thyroid tumors on diffusion-weighted MRI.Magnetic resonance in medicine · 2016Article
- Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories.Integrative biology : quantitative biosciences from nano to macro · 2015Article
- Classification of thyroid nodules using a resonance-frequency-based electrical impedance spectroscopy: a preliminary assessment.Thyroid : official journal of the American Thyroid Association · 2013Article
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Authors and funding
3 authors.
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Abstract
Statistical approach is a valuable way to describe texture primitives. The aim of this study is to design and implement a classifier framework to automatically identify the thyroid nodules from ultrasound images. Using rigorous mathematical foundations, this article focuses on developing a discriminative texture analysis method based on texture variations corresponding to four biological areas (normal thyroid, thyroid nodule, subcutaneous tissues, and trachea). Our research follows three steps: automatic extraction of the most discriminative first-order statistical texture features, building a classifier that automatically optimizes and selects the valuable features, and correlating significant texture parameters with the four biological areas of interest based on pixel classification and location characteristics. Twenty ultrasound images of normal thyroid and 20 that present thyroid nodules were used. The analysis involves both the whole thyroid ultrasound images and the region of interests (ROIs). The proposed system and the classification results are validated using the receiver operating characteristics which give a better overall view of the classification performance of methods. It is found that the proposed approach is capable of identifying thyroid nodules with a correct classification rate of 83 % when whole image is analyzed and with a percent of 91 % when the ROIs are analyzed.
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