Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images

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

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)55-62
Number of pages8
JournalComputers in Biology and Medicine
Volume95
DOIs
Publication statusPublished - 01-04-2018

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Fingerprint Dive into the research topics of 'Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images'. Together they form a unique fingerprint.

Cite this