Paper Info
Title
Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images.
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.
Year
DOI
Venue
2018
10.1016/j.compbiomed.2018.02.002
Computers in Biology and Medicine
Keywords
Field
DocType
Elongated quinary patterns,Higher order spectra,Particle swarm optimization,Support vector machine,Thyroid cancer,Ultrasound
CAD,Particle swarm optimization,Computer vision,Quinary,Pattern recognition,Computer science,Visual assessment,Support vector machine,Thyroid cancer,Artificial intelligence,Thyroid nodules,Ultrasound
Journal
Volume
ISSN
Citations 
95
0010-4825
6
PageRank 
References 
Authors
0.50
26
11
Name
Order
Citations
PageRank
U. Raghavendra11138.06
Anjan Gudigar2525.72
M. Maithri360.50
Arkadiusz Gertych4884.95
Kristen M Meiburger5485.08
Chai Hong Yeong6313.70
Chakri Madla760.84
Pailin Kongmebhol8141.30
Filippo Molinari947235.76
Kwan-Hoong Ng1023915.76
Rajendra Acharya U114666296.34