Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images
Document Type
Article
Publication Date
1-1-2018
Abstract
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.
Keywords
Elongated quinary patterns, Higher order spectra, Particle swarm optimization, Support vector machine, Thyroid cancer, Ultrasound
Divisions
fac_eng
Publication Title
Computers in Biology and Medicine
Volume
95
Publisher
Elsevier