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

Raghavendra, U. and Gudigar, Anjan and Maithri, Mangalore and Gertych, Arkadiusz and Meiburger, Kristen Mariko and Yeong, Chai Hong and Madla, Chakri and Kongmebhol, Pailin and Molinari, Filippo and Ng, Kwan Hoong and Acharya, U. Rajendra (2018) Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images. Computers in Biology and Medicine, 95. pp. 55-62. ISSN 0010-4825, DOI https://doi.org/10.1016/j.compbiomed.2018.02.002.

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Official URL: https://doi.org/10.1016/j.compbiomed.2018.02.002


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.

Item Type: Article
Uncontrolled Keywords: Elongated quinary patterns; Higher order spectra; Particle swarm optimization; Support vector machine; Thyroid cancer; Ultrasound
Subjects: R Medicine
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 18 Apr 2019 02:24
Last Modified: 03 Jan 2020 04:12
URI: http://eprints.um.edu.my/id/eprint/20993

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