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Computers in biology and medicine v.95, 2018년, pp.55 - 62   SCI SCIE SCOPUS
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Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images

Raghavendra, U.    (Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India   ); Gudigar, Anjan    (Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India   ); Maithri, M.    (Department of Mechatronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India   ); Gertych, Arkadiusz    (Department of Surgery, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA   ); Meiburger, Kristen M.    (Department of Electronics and Telecommunications, Politecnico di Torino, Italy   ); Yeong, Chai Hong    (Department of Biomedical Imaging, University of Malaya, Kuala Lumpur, Malaysia   ); Madla, Chakri    (Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand   ); Kongmebhol, Pailin    (Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand   ); Molinari, Filippo    (Department of Electronics and Telecommunications, Politecnico di Torino, Italy ;  ); Ng, Kwan Hoong   Acharya, U. Rajendra  
  • 초록  

    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. Highlights 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. Graphical abstract [DISPLAY OMISSION]


  • 주제어

    Elongated quinary patterns .   Higher order spectra .   Particle swarm optimization .   Support vector machine .   Thyroid cancer .   Ultrasound.  

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