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

• Published in: Computers in biology and medicine Vol. 95; pp. 55 - 62
• Main Authors: Raghavendra, U., Gudigar, Anjan, Maithri, M., Gertych, Arkadiusz, Meiburger, Kristen M., Yeong, Chai Hong, Madla, Chakri, Kongmebhol, Pailin, Molinari, Filippo, Ng, Kwan Hoong, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.04.2018; Elsevier Limited
• Subjects: Accuracy; Adult; Aged; Algorithms; Classification; Databases, Factual; Datasets; Diagnosis, Computer-Assisted - methods; Discriminant analysis; Elongated quinary patterns; Elongation; Entropy; Feature extraction; Female; Higher order spectra; Humans; Image Processing, Computer-Assisted - methods; Male; Middle Aged; Neural networks; Nodules; Particle swarm optimization; Principal components analysis; Support Vector Machine; Support vector machines; Thyroid; Thyroid cancer; Thyroid diseases; Thyroid Nodule - diagnostic imaging; Ultrasonic imaging; Ultrasonography; Ultrasound; Elongated quinary patterns; Thyroid cancer; Support vector machine; Higher order spectra; Particle swarm optimization; Ultrasound
• DOI: 10.1016/j.compbiomed.2018.02.002
• ISSN: 0010-4825

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. [Display omitted] •An expert system for the assessment of thyroid nodule is presented.•Both public and private datasets are used for the evaluation.•Multi-level elongated quinary patterns are used.•Particle swarm optimization (PSO) is used for feature selection.•Attained maximum accuracy of 97.71% using SVM classifier.