Please use this identifier to cite or link to this item:
Title: Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images
Authors: U. Raghavendra
Anjan Gudigar
M. Maithri
Arkadiusz Gertych
Kristen M. Meiburger
Chai Hong Yeong
Chakri Madla
Pailin Kongmebhol
Filippo Molinari
Kwan Hoong Ng
U. Rajendra Acharya
Keywords: Computer Science
Issue Date: 1-Apr-2018
Abstract: © 2018 Elsevier Ltd 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.
ISSN: 18790534
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.

Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.