Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images
Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be help...
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Published in | Computers in biology and medicine Vol. 95; pp. 55 - 62 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Ltd
01.04.2018
Elsevier Limited |
Subjects | |
Online Access | Get full text |
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Summary: | Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be helpful to cross-verify the severity of nodules. This paper proposes a new CAD system to characterize thyroid nodules using optimized multi-level elongated quinary patterns. In this study, higher order spectral (HOS) entropy features extracted from these patterns appropriately distinguished benign and malignant nodules under particle swarm optimization (PSO) and support vector machine (SVM) frameworks. Our CAD algorithm achieved a maximum accuracy of 97.71% and 97.01% in private and public datasets respectively. The evaluation of this CAD system on both private and public datasets confirmed its effectiveness as a secondary tool in assisting radiological findings.
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•An expert system for the assessment of thyroid nodule is presented.•Both public and private datasets are used for the evaluation.•Multi-level elongated quinary patterns are used.•Particle swarm optimization (PSO) is used for feature selection.•Attained maximum accuracy of 97.71% using SVM classifier. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0010-4825 1879-0534 |
DOI: | 10.1016/j.compbiomed.2018.02.002 |